applied sciences

Article Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves

Daniele Giordan 1 , Danilo Godone 1,* , Marco Baldo 1, Marco Piras 2 , Nives Grasso 2 and Raffaella Zerbetto 3

1 Italian National Research Council, Research Institute for Geo-Hydrological Protection (CNR-IRPI), Strada delle Cacce 73, 10135 Torino, Italy; [email protected] (D.G.); [email protected] (M.B.) 2 Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Torino, Italy; [email protected] (M.P.); [email protected] (N.G.) 3 Speleo Club Tanaro, Via Carrara, 12075 Garessio, Italy; [email protected] * Correspondence: [email protected]

Abstract: A three-dimensional survey of natural caves is often a difficult task due to the roughness of the investigated area and the problems of accessibility. Traditional adopted techniques allow a simplified acquisition of the topography of caves characterized by an oversimplification of the geometry. Nowadays, the advent of LiDAR and Structure from Motion applications eased three- dimensional surveys in different environments. In this paper, we present a comparison between other three-dimensional survey systems, namely a Terrestrial Laser Scanner, a SLAM-based portable instrument, and a commercial photo camera, to test their possible deployment in natural caves survey. We presented a comparative test carried out in a tunnel stretch to calibrate the instrumentation on a



 benchmark site. The choice of the site is motivated by its regular geometry and easy accessibility. According to the result obtained in the calibration site, we presented a methodology, based on the Citation: Giordan, D.; Godone, D.; Structure from Motion approach that resulted in the best compromise among accuracy, feasibility, Baldo, M.; Piras, M.; Grasso, N.; and cost-effectiveness, that could be adopted for the three-dimensional survey of complex natural Zerbetto, R. Survey Solutions for 3D Acquisition and Representation of caves using a sequence of images and the structure from motion algorithm. The methods consider Artificial and Natural Caves. Appl. two different approaches to obtain a low resolution complete three-dimensional model of the cave Sci. 2021, 11, 6482. https://doi.org/ and ultra-detailed models of most peculiar cave morphological elements. The proposed system 10.3390/app11146482 was tested in the Gazzano Cave (Piemonte region, Northwestern Italy). The obtained result is a three-dimensional model of the cave at low resolution due to the site’s extension and the remarkable Academic Editors: Cesareo amount of data. Additionally, a peculiar speleothem, i.e., a stalagmite, in the cave was surveyed at Saiz-Jimenez and Daniel Dias high resolution to test the proposed high-resolution approach on a single object. The benchmark and the cave trials allowed a better definition of the instrumentation choice for underground surveys Received: 14 May 2021 regarding accuracy and feasibility. Accepted: 11 July 2021 Published: 14 July 2021 Keywords: structure from motion; LiDAR technology; underground survey; speleology; GPS denied environment; 3D cadastral map of caves Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Cave surveys are a crucial point in cavers’ activity as they allow mapping caves for their exploration [1]. It is helpful as a guide for further exploitation or as a basis for scientific activities carried out in those caves. In particular, 3D digital models of caves are suitable Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. tools for both scientific purposes. They can be used for geological or geomorphological This article is an open access article observations [2], for the integration of 3D underground caves cadastral databases [3], and distributed under the terms and visualization and navigation, especially related to complex access caves, through virtual conditions of the Creative Commons and augmented reality techniques [4]. In geomatics, several well-established methods Attribution (CC BY) license (https:// are available for the three-dimensional survey of environments. Indoor environments creativecommons.org/licenses/by/ represent a rather critical case, where the GNSS positioning technique fails [5]. There- 4.0/). fore the 3D survey should be carried out by well-established topographical instruments,

Appl. Sci. 2021, 11, 6482. https://doi.org/10.3390/app11146482 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, x FOR PEER REVIEW 2 of 21

of measuring angles and distances such as the total station. Still, the space complexity limits the use of some instruments for 3D surveying or requires the operator’s great expe- rience. Additionally, the three-dimensional representation of the caves is even more com- plex. The task is complicated by extreme environmental conditions, such as low/variable illumination, humidity, and limited accessibility [6]. Traditionally, the mapping procedures in underground cave environments were car- ried out by tracing a three-dimensional polyline by using clinometers and compass to store the different angles featuring the survey; each segment implied the marking of its vertices on cave walls to serve as a reference. The cave outline was then manually plotted Appl. Sci. 2021, 11, 6482 2 of 21 by the caver with a remarkable level of subjectivity (Figure 1) [1,7], including systematic and random errors. Recently, the use of clinometers and compass was substituted by the spread of capablehandheld of topographic measuring angles instruments and distances such as such portable as the laser total range station. finders Still, theequipped space com-with plexitydigital clinometers limits the use and of compass some instruments [1,7]. The formeasurements 3D surveying acquired or requires with the operator’slaser range greatfinder experience. are stored in Additionally, the device or, the currently, three-dimensional in a phone representationapp (e.g., TopoDroid) of the caves linked is to even the morerange complex.finder where The the task caver is complicated can visualize by the extreme entire survey environmental [8]. Moreover, conditions, in every such station as low/variablepoint, the survey illumination, procedure humidity, implies a and series limited of circular accessibility measures [6]. to trace a transverse sec- tion Traditionally,of the cave obtaining the mapping a discrete procedures three-dimensional in underground representation. cave environments The app allows were car- the riedcaver out to bymanually tracing adraw three-dimensional the cave outline polyline adding by details using clinometersto the survey and (Figure compass 1). toThe store ob- thetained different final result angles is featuring a subjecti theve representation survey; each segment of the cave implied done the by markingthe caver ofon its a network vertices onof measured cave walls points. to serve The as resolution a reference. and The accuracy cave outline of the wasthree-dimensional then manually model plotted are by often the caverhighly with heterogeneous a remarkable and level rely of on subjectivity the caver’s (Figureability 1that)[ 1 connects,7], including the sequence systematic of anddots randomand creates errors. the representation of the surveyed area.

FigureFigure 1.1. ExampleExample ofof aa cavecave surveysurvey basedbased onon thethe laserlaser rangerange finderfinder approach;approach; thethe post-processing post-processing software adopted by cavers can be used for a coarse 3D reconstruction of the cave. The model pre- software adopted by cavers can be used for a coarse 3D reconstruction of the cave. The model sents the Gazzano cave that is the case study that is described and discussed in this manuscript. presents the Gazzano cave that is the case study that is described and discussed in this manuscript.

Recently,Thanks to the technological use of clinometers advances and over compass the last was decade, substituted several by the3D spreadsurveying of hand- tech- heldniques topographic for generating instruments full 3D suchand multi-resolution as portable laser models range finders of underground equippedwith cavities digital are clinometersnow available. and Besides compass topographical [1,7]. The measurements instruments, such acquired as Total with Stations the laser (TS), range it is possible finder are stored in the device or, currently, in a phone app (e.g., TopoDroid) linked to the range finder where the caver can visualize the entire survey [8]. Moreover, in every station point, the survey procedure implies a series of circular measures to trace a transverse section of the cave obtaining a discrete three-dimensional representation. The app allows the caver to manually draw the cave outline adding details to the survey (Figure1). The obtained final result is a subjective representation of the cave done by the caver on a network of measured points. The resolution and accuracy of the three-dimensional model are often highly heterogeneous and rely on the caver’s ability that connects the sequence of dots and creates the representation of the surveyed area. Thanks to technological advances over the last decade, several 3D surveying tech- niques for generating full 3D and multi-resolution models of underground cavities are now available. Besides topographical instruments, such as Total Stations (TS), it is pos- Appl. Sci. 2021, 11, 6482 3 of 21

sible to get 3D information about the environment in the form of point clouds quickly and easily and with high levels of details thanks to a wide range of passive and active sensors [9]. These sensors are now commonly used in different application fields (e.g., cultural heritage, monitoring, etc.) for the reconstruction of scenes or 3D objects and there are several studies aimed at comparing the use of different techniques to highlight their advantages and criticality and to define the best practice to follow [10–15]. To achieve more accurate and complete surveying results, it is crucial to be aware of the properties that certain technique needs to respond and of the requirements of the desired results [16]. Firstly, the selection of the most appropriate survey approach should consider not only the precision and the reliability of the resulting products, but also the size and manageability of the final datasets. Then, from the operational point of view, it is fundamental to evaluate the portability of instruments, especially in underground sites with limited accessibility. Other important elements are the required acquisition time and the autonomy of the power system of selected survey solutions. Finally, the conditions of the survey environment greatly influence the choice of the most appropriate technique, especially in the case of underground caves, where the difficulty of access, humidity, temperature, and lighting conditions strongly reduced the number of effective solutions. Both active and passive remote sensing techniques are, nowadays, widely exploited in underground environments surveys for 3D modeling purposes. Active remote sensing- based instruments include all the scanning devices, such as Terrestrial Laser Scanners (TLS), emitting laser pulses [17]. A surface or object reflects each pulse, and the instrument is capable of computing the distance and position, in polar coordinates, of the point. The intensity of the echo is registered, too. According to its performance level and its settings, the instrument can repeat this sequence from 10,000 to 500,000 times per second. Moreover, to acquire a complete survey of the surroundings, laser beams are projected in several directions using rotating and/or oscillating mirrors. The color information can also be recorded thanks to the integration of the system with a digital camera. For each scan station, raw output data is generated in the form of a point cloud. The data registration is carried out by positioning reference targets (natural or artificial), which were previously surveyed by a topographical survey, to georeference the resulting point cloud in a specific reference system [18]. TLS is now a consolidated approach to obtain a 3D measurement. Despite its encumbrance and weight, some tentative approaches to use it in cave surveys were attempted. They were especially tried in caves open to tourists and thus equipped with stairs and footbridges, easing the transport of the instrumentation, as evidenced by several research works [19–25] or in sites relevant for scientific purposes such as the Naica cave [26]. TLS allows capturing highly accurate data even at long distances but, in indoor environments and complex areas characterized by limited lines of sight and many shadow areas, it often cannot be an efficient method given the need for a high number of station points. Thanks to the high level of detail obtained with this technique, TLS has been used in many studies to detect limited portions of caves, to conduct morphological and structural analysis [27–30]. Only in some works, as mentioned by [31], TLS were used to make surveys of entire caves, with extensions greater than a few hundred meters. Although these surveys allowed to obtain detailed models, they demonstrated that, due to the huge amount of final data, the point clouds must be divided into parts to be analyzed or sub-sampled, with a consequent loss of resolution. To overcome these applicability issues, in recent years, a new generation of range- based profilers was pushed on the market. This new generation of instruments is rep- resented by integrated and handheld solutions called Indoor Mobile Mapping Systems (IMMS) [32]. These systems implement multiple sensors, such as profilers, Inertial Mea- surement Units (IMU), Distance Measuring Instruments (DMI), and, in some cases, camera sensors, in a unique platform. The portability and flexibility of these solutions make them particularly suitable for indoor mapping applications [33,34]. These instruments are gen- erally based on Simultaneous Localization and Mapping (SLAM) technology, including specific algorithms to solve the problem of localization, which allow to, simultaneously, es- Appl. Sci. 2021, 11, 6482 4 of 21

timate the instrument position and generate a 3D digital model of the environment [35,36]. Consequently, SLAM technology makes IMMS a competitive possibility in terms of time consumption in data acquisition and data processing. Several IMMS solutions are now available on the market. Among the different types, many of them are integrated on vehi- cles, trolleys, backpacks, or handheld platforms [33,37], and several researchers analyzed their performances [37–39]. The maneuverability of these tools makes them particularly suitable for indoor applications and narrow spaces [40,41], even in environments with limited access, which may require a certain speed in the acquisition phase, as could be the case of caves or mines [42,43]. Among passive solutions 3D information can be extracted, thanks to different ap- proaches involving classical photogrammetric technique [44] or Structure from Motion (SfM) algorithms [45,46] from a sequence of images. The SfM approach is based on the capture of multiple shots that portrays the surveyed object or area. The algorithms de- veloped in the computer vision field and widespread in RPAS (Remotely Piloted Aircraft Systems) applications [47] exploit the photogrammetry principles and allow the 3D re- construction of the surveyed target [48]. The algorithm’s capability allows reconstructing the surveyed object even from pictures taken for other aims (i.e., reconnaissance photos) sufficiently overlapping (70–80% of overlap), allowing to exploit archived dataset and obtain unprecedented datasets [49]. SfM can be considered a valid and low-cost solution for the acquisition of 3D models that can be considered when it is impossible to use other well-known systems like TLS or other topographic solutions [50]. Furthermore, xxx by knowing the internal and external orientation of a digital image, using a dense 3D digital model, it is possible to associate the value of the distance between the center and the object to the same pixel. This procedure allows realizing a solid image, which is, therefore, the result of the integration of the RGB digital image with distance, allowing the restitution of the third dimension [51]. The reconstruction of the photogrammetric point cloud can also be registered with ref- erence points to be plotted in geographical coordinates. The advances in sensors miniatur- ization and optimization have allowed developing portable instruments that are currently at an experimental level trying to substitute the current ones. Today, image sensors are integrated into commonly used devices, such as action cameras and smartphones, giving, even to non-expert operators, the ability to exploit collected data for the generation of 3D models [6]. Moreover, the increment of RPAS datasets is additionally broadening the potential application of such an approach [52,53]. Although these systems were initially im- plemented to acquire data in extensive outdoor environments, there are now many recent studies that describe their applicability in indoor spaces [54,55], even in dark caves [56]; in this last case, proposed solutions are mainly employed for the survey of small portions of the natural cave [57] and not for extensive surveys. The main advantage of the use of passive sensors is that they do not require elaborate data acquisition planning, and data processing is also relatively easy. The resolution of the generated 3D point clouds strictly depends on: the image resolution, the camera characteristics, and the acquisition distance from the studied object, namely by the Ground Sample Distance (GSD), which can be calculated as follows:

hg·ρ GSD = , (1) ck

where hg is the flying height above ground (or the acquisition distance from the object), ck is the focal length of the camera and ρ is the image’s pixel size [58]. Even if the density of point clouds can be compared to that of the TLS, it may be affected by a certain level of noise in particular with low light conditions, light variations, and reflective or uniform surfaces. The recent evolution of SfM applications on caves seems to be encouraging for the identification of a methodology that could be used for a massive 3D mapping of caves. To date, only a minimal number of caves are represented in the third dimension and they Appl. Sci. 2021, 11, 6482 5 of 21

are often equipped with ladders and walkways, for tourism or archaeological purposes, to ease their access [59,60]. In this paper, we investigated the best compromise between the accuracy and feasibility of different techniques and to define a general methodology to carry out a 3D survey of natural and complex caves. The proposed methodology should be easily replicable and ensure sufficient precision, such as to be able to populate a possible 3D cadastral map of the caves or carry out virtual tours, even for difficult to access environments. In fact, this type of application requires a user-friendly methodology that refers to simple concepts and allows a high diffusion of the technique. The paper is organized as follows: we made an accurate survey of a test site, i.e., a road tunnel segment to compare the proposed techniques with the reference data and assess their accuracy. According to the test site results, we developed and proposed a methodology for the survey of natural caves based on the SfM approach. The proposed survey technique is tested in the Gazzano cave. The final discussion presents an analysis of the different 3D survey methodologies and the best solution for the 3D natural caves survey, showing the general validity of the SfM approach.

2. Material and Methods The paper presented two different case studies; the first part of the research is focused on the definition of the quality of a 3D point cloud of an underground artificial site. We tested three different measuring systems and we explored, in particular, the quality (in terms of precision) of their results and their limitations in a segment of a tunnel to adopt this approach in an underground structure that can be easily reached even using cars and that have the typical limitations of underground sites like the lack of GNSS signal. The second presented case study is the Gazzano cave. Gazzano is an interesting cave located in Piemonte (NW of Italy) that has been selected for the application of the methodology developed for the creation of a 3D model of caves.

2.1. Artificial Tunnel Segment To test the capability of reconstructing an underground structure, we analyzed the survey of the tunnel test site assuming the TLS survey as the reference technique. The considered tunnel segment is 100 m long with 12.5 m diameter, where we placed and measured on walls the position of 56 reference targets. Targets were distributed in groups of 4, 2 for each side, every 10 m along the tunnel. Within the subgroup, the target-target distance ranged from 1.5 to 3 m. Targets position was rigorously surveyed from 6 setup locations using Total Station (TOPCON GPT-7001L) to compute their 3D coordinates. The survey was registered using four benchmarks located in the tunnel. The network coordinates were computed with a precision of ±0.02 m.

2.2. Gazzano Cave The Gazzano cave (125 Pi/CN—Regional cave cadastre) is located in the Maritime Alps in the municipality of Garessio, Northwestern Italy (44◦11052.1800 N, 8◦0040.7400 E) at 574 m above sea level elevation (Figure2). The cave was accidentally discovered during the excavation of a quarry and explored in 1898. In the past, several peculiar features were looted and, nowadays, the marks of these actions are still visible. The Gazzano cave is an important natural site for the preservation of biological diversity because it is a bat (Rhinolopus spp.) hibernation site and a natural refuge for European cave salamanders (Speleomantes spp.). Appl. Sci. Sci. 20212021, 1111, x FOR PEER REVIEW 6 of 21 , , 6482 6 of 21

Figure 2. (a) Gazzano cave cross-section from the traditional survey; (b) map of the cave with the polygonal survey; inset) Figure 2. (a) Gazzano cave cross-section from the traditional survey; (b) map of the cave with the polygonal survey; inset) Gazzano location map. Gazzano location map. The cave, excavated in dolomitic limestone, is characterized by a unique corridor approximatelyThe cave, excavated 105 m long in thatdolomitic ends inlimestone, a chamber is 37characterized m high with by a a waterfall unique corridor from its ap- top proximately(Figure2a); the 105 water m long forms that a ends small in lake a chamber in the chamber 37 m high then with streams a waterfall through from the wholeits top (Figurecave length 2a); the until water the exitforms (Figure a small2b). lake The incave the chamber is accessible thenafter streams a short through walk. the Initially, whole caveapproximately length until in the firstexit third(Figure of its2b). length, The cave the cave is accessible is characterized after a byshort a reduced walk. Initially, height— approximatelyminimum ~1 m in hampering the first third the accessibilityof its length, and the operation cave is characterized with bulky instrumentation; by a reduced height—minimumthen the height increases ~1 m hampering allowing the one accessibility to walk almost and operation comfortably with untilbulky the instrumen- terminal tation;chamber. then the height increases allowing one to walk almost comfortably until the termi- nal chamber. 2.3. D Survey of the Artificial Tunnel Segment 2.3. DThe Survey survey of the of Artificial the tunnel Tunnel was doneSegment considering three approaches based on the use of activeThe or survey passive of instruments the tunnel was with done different consider costsing and three characteristics. approaches Thebased first on considered the use of activesystem or is passive a terrestrial instruments laser scanner with different (TLS). It costs represents and characteristics. the most accurate The first and considered expensive systemsolution is for a terrestrial the acquisition laser ofscanner a 3D model.(TLS). It Despite represents these the systems most allowaccurate to obtain and expensive products solutionwith high for resolutions, the acquisition their of size a 3D and model. weight Despite make them these difficult systems to allow transport to obtain in many products caves, withwhere high the accessibilityresolutions, andtheir movement size and weight within themmake are them limited. difficult The secondto transport category in many of the caves,considered where instrument the accessibility is portable and LiDARmovement (PLS). within PLS arethem characterized are limited. by The an activesecond sensor cate- gorythat worksof the likeconsidered the TLS, instrument but the system is portable has been LiDAR designed (PLS). to PLS be more are characterized portable and by easier an activeto use. sensor The limited that works cost andlike the dimension TLS, but make the system this category has been of designed instruments to be more more adapted porta- bleto theand acquisition easier to use. of environmentsThe limited cost characterized and dimension by problems make this of ca accessibility.tegory of instruments The third moreconsidered adapted solution to the isacquisition based on theof environmen use of a commercialts characterized photo cameraby problems and Structure of accessibil- from ity.Motion The third (SfM) considered for the generation solution ofis based a 3D point on the cloud. use of This a commercial is the cheapest photo solution camera and that Structureadopts a passivefrom Motion sensor. (SfM) Table for1 shows the generation the instruments of a 3D exploited point cloud. for thisThis research is the cheapest work. solution that adopts a passive sensor. Table 1 shows the instruments exploited for this research work.

Appl. Sci. 2021, 11, 6482 7 of 21 Appl. Sci. 2021, 11Appl., x FOR Sci. PEERAppl. 2021 Sci.,REVIEW 11 ,2021 x FOR, 11 PEER, x FOR REVIEW PEER REVIEW 7 of 21 7 of 21 7 of 21

Table 1. Adopted instrumentation for the acquisition of the 3D model of the test site. Table 1. AdoptedTable instrumentation 1.Table Adopted 1. Adopted instrumentation for the instrumentation acquisition for theof the acquisitionfor 3D the model acquisition of of the the 3D oftest modelthe site. 3D ofmodel the test of the site. test site. TLS PLS Photo Camera (SfM) TLS TLS TLS PLS PLS PLS Photo Camera (SfM) Photo Camera Photo Camera (SfM) (SfM) RIEGL LMS-Z420iRIEGL RIEGLLMS-Z420iRIEGL LMS-Z420i LMS-Z420i Kaarta Stencil 2–16 Kaarta KaartaStencil 2–16Stencil 2–16 Canon Canon EOSEOS 100D100D Canon cameracamera EOS Canon 100D EOS camera 100D camera

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The of materialization innertures. benchmarks Thetures. materialization ofThe inner should materialization benchmarks be carefullyof inner of should benchmarks inner realized bebenchmarks carefully without shoulddamaging realized should be carefully bewithout cave carefully realized dam- realized without without dam- dam- featuresaging and by cave minimizing featuresaging andcaveaging the by impact; features cave minimizing features for and these by the and minimizing reasons, impact; by minimizing a for small the these impact; varnish thereasons, impact; for mark these a small for with reasons, these varnish color reasons, a smallmark a varnish small varnish mark mark in contrastwith with color the in surroundings contrastwith colorwith with in color (e.g., thecontrast surroundings redin contrast or with orange) the with surroundings(e can.g., the be red surroundings sufficient or orange) (e.g., because red can(e.g., or be theredorange) sufficient distance or orange) can because be cansufficient be sufficient because because betweenthe the distance target andbetweenthe thedistancethe photo the distance target between camera and between isthe the always target photo the limited.targetand camera the and photo Additionally,is thealways photocamera limited. camera is to always maximize Additionally, is always limited. limited. Additionally,to Additionally, to to its accuracy,maximize the polygonal its accuracy,maximize surveymaximize the its polygonalaccuracy, should its accuracy, be the survey carried polygonal the should on polygonal twice survey be carried in survey bothshould on directions should twicebe carried in be toboth carried closeon directionstwice on in twice both in directions both directions the polygon.to close The the survey polygon.to close phaseto The the close of surveypolygon. benchmarks the polygon. phase The ofsurvey is Thebe performednchmarks survey phase of usingphase is be performednchmarks aof portable benchmarks using is rangeperformed a is portable finder performed using range a using portable a portable range range capable of measuring and storing angles and distances contemporarily. The position of finder capablefinder of measuring findercapable capable ofand measuring storing of measuring angles and storing and and distances storing angles anglesand contemporarily. distances and distances contemporarily. The contemporarily. posi- The posi- The posi- external reference points can be acquired using GNSS systems. tion of externaltion reference oftion external of points external reference can referencebe pointsacquired pointscan using be canacquired GNSS be acquired systems. using GNSSusing GNSSsystems. systems. The subsequentThe phase subsequentThe is subsequentdedicated phase to phaseis the dedicated capture is dedicated to of the the captureto cave the bycapture of the the camera. caveof the by caveImages the bycamera. the camera. Images Images should be capturedshould should inbe accordance captured be captured in with accordance inthe accordance principles with the withof SfMprinciples the thus principles varyingof SfM of thusthe SfM capture varying thus varying the capture the capture position in everyposition pictureposition in everyand, in contemporarily pictureevery picture and, contemporarily and,, assuring contemporarily an adequate, assuring, assuring overlap. an adequate an As adequate the overlap. op- overlap. As the As op- the op- eration is carriederation outeration isin carried a completely is carried out in outmanuala completely in a way,completely manualthe capture manual way, should the way, capture followthe capture should a squiggly should follow followa squiggly a squiggly path (Figure 4)path with (Figure paththe camera (Figure 4) with framing 4) the with camera the cavecamera framing in directionframing the cave the of in the cave direction operator in direction of path. the operatorof Due the to operator path. Due path. to Due to the extreme variationthe extremethe of extremethe variation cave’s variation size of the and cave’sof shape, the cave’ssize it is and impossible size shape, and shape,it tois impossiblestrictly it is impossible define to strictlya priori to strictly define definea priori a priori

Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 21

acquisition parameters. The most critical elements are the required overlap (more than 70%) and the illumination of images; according to our experience, the use of flash is strongly discouraged as it ‘doesn’t assure a diffuse illumination necessary for an adequate rendering of the cave in post-processing. Concerning the camera settings, they should vary according to the lighting conditions and size of the cave. The best way to illuminate the cave is to exploit the light mounted on the ‘caver’s helmet and, when it is possible, to place another operator ahead illuminating in the opposite direction. The device used in the current experiment is a superkikko (kikkolamp.com) set at 200 lumens in the corridors and 1400 lumen in the main chamber. In any case, the illumination should be indirect to avoid blinding effects on the camera sensor. The two presented phases are tightly con- nected and of high importance for the correct acquisition of the image sequence. Only with a good overlap and complete coverage of the cave, it is possible to obtain an excellent final result. Lastly, in the post-processing phase, all the acquired data (i.e., ‘benchmarks’ coordinates and image sequence) are imported into the processing software where the cave 3D model is computed. The processing, which is subdivided into several stages, starts with recognizing and collimating reference points in every picture manually depict- ing them. Then, all the images are aligned according to their relative positions and input coordinates. It is essential to point out that, as the acquisition phase is done underground, the images are not provided by a shooting point coordinate. The aligned image set is then used to compute the 3D point cloud and the final model with mosaicked images draped on, resulting in the 3D reconstruction of the cave. Using the same approach, it is possible to obtain a centimetric-resolution 3D model of the most interesting elements located inside the cave. In this case, the acquisition phase should be done by providing an ultra-high-resolution photo sequence of the target, ob- tained with a high-end digital camera. Increasing the resolution of the acquired data and processing settings it is possible to create a more detailed 3D model of the interior ele- Appl. Sci. 2021, 11, 6482 ments of the caves and provide a double-resolution model: a general medium-scale rep-8 of 21 resentation of the caves (1:500–1:1000 scale) with ultra-detailed models of the main ele- ments of the cave at a centimeter resolution.

Figure 3. workflow of the proposed methodology. Figure 3. Workflow of the proposed methodology.

The subsequent phase is dedicated to the capture of the cave by the camera. Images should be captured in accordance with the principles of SfM thus varying the capture position in every picture and, contemporarily, assuring an adequate overlap. As the operation is carried out in a completely manual way, the capture should follow a squiggly path (Figure4) with the camera framing the cave in direction of the operator path. Due to the extreme variation of the cave’s size and shape, it is impossible to strictly define a

priori acquisition parameters. The most critical elements are the required overlap (more than 70%) and the illumination of images; according to our experience, the use of flash is strongly discouraged as it doesn’t assure a diffuse illumination necessary for an adequate rendering of the cave in post-processing. Concerning the camera settings, they should vary according to the lighting conditions and size of the cave. The best way to illuminate the cave is to exploit the light mounted on the caver’s helmet and, when it is possible, to place another operator ahead illuminating in the opposite direction. The device used in the current experiment is a superkikko (kikkolamp.com) set at 200 lumens in the corridors and 1400 lumen in the main chamber. In any case, the illumination should be indirect to avoid blinding effects on the camera sensor. The two presented phases are tightly connected and of high importance for the correct acquisition of the image sequence. Only with a good overlap and complete coverage of the cave, it is possible to obtain an excellent final result. Lastly, in the post-processing phase, all the acquired data (i.e., benchmarks’ coordinates and image sequence) are imported into the processing software where the cave 3D model is computed. The processing, which is subdivided into several stages, starts with recognizing and collimating reference points in every picture manually depicting them. Then, all the images are aligned according to their relative positions and input coordinates. It is essential to point out that, as the acquisition phase is done underground, the images are not provided by a shooting point coordinate. The aligned image set is then used to compute the 3D point cloud and the final model with mosaicked images draped on, resulting in the 3D reconstruction of the cave. Appl. Sci. 2021, 11, 6482 9 of 21 Appl. Sci. 2021, 11, x FOR PEER REVIEW 9 of 21

Figure 4. Scheme of the cave survey.

UsingThe medium the same resolution approach, approach it is possible is mainly to obtain finalized a centimetric-resolution to the acquisition of 3Da continu- model ofous the 3D mostmodel interesting of the cave. elements This model’s located final inside resolution the cave. is sufficient In this case,for a thedetailed acquisition recon- phasestruction should of the be morphology done by providing of the cave an ultra-high-resolution but not so clear to obtain photo a sequence correct visualization of the target, obtainedof the speleothems with a high-end or the other digital essential camera. elem Increasingents that the often resolution enrich ofcaves. the acquiredThe approach data andadopted processing for the ultra-high-resolution settings it is possible model to create acquisition a more detailed is similar. 3D Still, model the number of the interior of im- elementsages should of thebe sufficient caves and to provideguarantee a double-resolutionboth the complete model:description a general of the medium-scaleobject and the representationcapture of its character of the caves details. (1:500–1:1000 Their Grou scale)nd Sample with ultra-detailedDistances (GSD) models should of thebe lower main elements(up to millimeters) of the cave [64]. at a centimeter resolution. TheThese medium are the resolutionprinciples approach that must is guide mainly the finalized operator to in the the acquisition images capture of a continuous to assure 3Dthe modelcorrect of survey the cave. resolution This model’s of specific finalresolution areas or objects is sufficient within for an a detailedunderground reconstruction cave: in- ofdeed, the it morphology is not possible of theto define cave buta general not so acquisition clear to obtain scheme a correct for these visualization types of features, of the speleothemsgiven their irregularity or the other and essential variability elements in the that morphology often enrich of caves. the features, The approach but the adopted survey formust the be ultra-high-resolution adapted from time to model time, trying acquisition to cover is similar.the entire Still, area the of numberthe object. of To images scale shouldthe model, be sufficientas previously to guarantee stated, it both is necessary the complete to know description the relative of the position object of and some the capturespread materialized of its character REF details. points Their on the Ground study Samplearea. Distances (GSD) should be lower (up to millimeters)In the case [ 64of]. 3D acquisition of natural elements of particular value, it is helpful to exploitThese removable are the principlesmarkers to that guarantee must guide the pres theervation operator of in the the surveyed images capture elements to or assure spe- theleothems. correct Due survey to the resolution limited space of specific that often areas characterized or objects withinthe caves, an the underground measurement cave: of indeed, it is not possible to define a general acquisition scheme for these types of features, these points may be difficult, if not impossible, even with a portable range finder: in these given their irregularity and variability in the morphology of the features, but the survey cases, it is appropriate to take advantage of templates of known size to ensure proper must be adapted from time to time, trying to cover the entire area of the object. To scale the scalability of the model to reduce the number of required reference points. model, as previously stated, it is necessary to know the relative position of some spread materialized REF points on the study area. 3. Obtained Results In the case of 3D acquisition of natural elements of particular value, it is helpful to exploit3.1. D Models removable of the Artificial markers Tunnel to guarantee Segment the Comparison preservation of the surveyed elements or speleothems.The TLS Duesurvey, to the operated limited by space a RIEGL that oftenLMS-Z420i, characterized was subdivided the caves, into the measurementsix scan posi- oftions these evenly points distributed may be difficult,along the ifmidline not impossible, of the tunnel even section with to a portablemaximize range the visibility finder: inof thesethe surveyed cases, it area. is appropriate In the post-processing to take advantage phase, of each templates scan was of knownco-registered size to and ensure roto-trans- proper scalabilitylated based of on the cylindrical model to targets reduce [18]. the numberThen, the of whole required point reference cloud (83,932,625 points. points) was registered, in Microstation Connect Edition, to the visible targets among the 56 available 3.(Table Obtained 2). Each Results scan was fitted with a multi-station algorithm to improve surface coplanarity 3.1.in overlapping D Models of areas the Artificial by introducing Tunnel Segmenta scale fact Comparisonor based on the cloning effect of homologous geometries.The TLS The survey, overall operated procedure by aaccuracy RIEGL LMS-Z420i,results in anwas RMS subdivided of 0.025 m, intofollowing six scan the posi- tar- tionsget survey, evenly confirming distributed its along reliability the midline as a benchm of theark tunnel for the section next phase to maximize of the investigation. the visibility of the surveyed area. In the post-processing phase, each scan was co-registered and roto- translatedTable 2. TLS based scans on precision. cylindrical targets [18]. Then, the whole point cloud (83,932,625 points) was registered, in Microstation Connect Edition, to the visible targets among the 56 avail- Scanposition Tie Points RMS 3D [m] Distance [m] able (Table2). Each scan was fitted with a multi-station algorithm to improve surface 1 8 0.007 - coplanarity in overlapping areas by introducing a scale factor based on the cloning effect 2 6 0.016 19.36 of homologous geometries. The overall procedure accuracy results in an RMS of 0.025 m, 3 8 0.011 17.33

Appl. Sci. 2021, 11, 6482 10 of 21

following the target survey, confirming its reliability as a benchmark for the next phase of the investigation.

Table 2. TLS scans precision.

Scanposition Tie Points RMS 3D [m] Distance [m] 1 8 0.007 - 2 6 0.016 19.36 3 8 0.011 17.33 4 4 0.010 23.68 5 6 0.036 14.90 6 8 0.007 23.84

Consequent to the TLS survey, PLS and SfM ones were carried out on the same test site. The PLS survey was performed with a Kaarta Stencil 2–16 portable laser scanner, based on the SLAM algorithm [65]. The platform integrates a Velodyne VLP-16 LiDAR system composed of 16 rotating profilers (each with a different orientation with a Field of View (FOV) equal to 360◦Hx30◦V), a low-cost IMU, and an optical sensor. The Stencil 2 is based on the Visual-LiDAR Odometry and Mapping approach [66]. The main workflow provides, firstly, a visual-inertial odometry algorithm, performing a frame to frame motion estimation, which combines each future to the depth data derived by the LiDAR and optimizes the solution considering the IMU information; then, the LiDAR odometry algorithms exploit speed data measured by the odometer for the point cloud registration. Finally, the identification and matching of geometric features in the point cloud allow optimizing the registration, using the previously estimated pose constraints. Thanks to this approach, Stencil 2 can generate a point cloud of the surveyed environment, registered in a local system, in real-time. Data acquisition was performed following a straight path, which, like SfM, was covered back and forth, adding segments at the beginning and end of the test section to survey its terminal portions better. The raw data acquired were firstly processed using the Adaptive Data Reply tool, provided by Stencil 2. The tool allows to simulate the scanning at a lower speed and possibly change several parameters to optimize the scan matching process. The resulting point cloud was roto-translated in the same reference system of the TLS one by employing the previously surveyed reference targets, and software obtaining the residuals showed in Table3; the quality of the results allowed to proceed comparison with the reference cloud.

Table 3. PLS co-registering errors.

RMSE X (m) RMSE Y (m) RMSE Z (m) 0.0195 0.0063 0.0031

The SfM was performed by acquiring a sequence of images with a Canon EOS 100D camera along a squiggly path, back and forth, in the tunnel’s main direction. The position of each capture and the path were selected arbitrarily during the acquisition. The whole set of 245 images was then imported in Agisoft Metashape to reconstruct the point cloud by the employment of the SfM algorithm. Preliminary, the coordinates of the targets were imported. Their position was collimated on images to co-register the resulting point cloud (Table4) to the TLS one and allow the comparison. The whole process took approximately 90 min of calculation time. Appl. Sci. 2021, 11, 6482 11 of 21

Table 4. SfM co-registering errors.

Targets RMSE X (m) RMSE Y (m) RMSE Z (m) RMSE XY (m) Total RMSE (m) 12 0.009 0.012 0.011 0.015 0.019

The comparison between the PLS and TLS clouds was carried out by analyzing three Appl. Sci. 2021, 11, x FOR PEER REVIEWsections (Start, Center, End) of the tunnels by manually measuring distances between11 of 21 homologous features in the two clouds (Figure5). The residuals are listed in Table5.

FigureFigure 5. 5.Start Start section, section, measurements measurements (orange: (orange: TLS TLS cloud; cloud; black: black: PLS PLS cloud). cloud).

Table 5. PLS/TLS comparison results. Table 5. PLS/TLS comparison results. Section\Subcloud ΔA [m] ΔB [m] ΔC [m] Section\Subcloud ∆A [m] ∆B [m] ∆C [m] Start 0.02 0.04 0.10 Start 0.02Center 0.040.03 0.06 0.10 0.01 Center 0.03End 0.060.04 0.10 0.01 0.08 End 0.04 0.10 0.08 To check the reliability of the SFM point cloud (73,188,930 points), the identical cross- sections (Figure 6) were extracted and compared with the homologous features of the TLS one Toobtaining check the the reliability residuals ofsummarized the SFM point in Table cloud 6. (73,188,930 points), the identical cross- sections (Figure6) were extracted and compared with the homologous features of the TLS oneTable obtaining 6. SfM/TLS the comparison residuals summarizedresults. in Table6.

ΔA [m] ΔB [m] Start 0.07 0.05 Center 0.01 0.05 End 0.02 0.04 Appl.Appl. Sci. Sci.20212021, 11, x11 FOR, 6482 PEER REVIEW 12 of12 21 of 21

FigureFigure 6. Center 6. Center section section measurements measurements (red (red:: TLS TLS cloud; cloud; black: black: SfM SfM cloud). cloud).

3.2.Table D reconstruction 6. SfM/TLS comparison of the Gazzano results. Cave The cave was surveyed according to the proposed methodology (described in Section ∆A [m] ∆B [m] 2.4), validated as a result of the surveys in the tunnel. The entire image dataset was cap- tured accordingStart to the following camera settings: 0.07 i) focal length 18 mm; iso 6400; 0.05 exposure time 1/13 sec; Centerf-number 3.5 and processed in 0.01 Agisoft Metashape where the 0.05 point cloud was reconstructed.End Prior to the calculation, th 0.02e images were registered to the 0.04 cave survey (Figure 2b) to obtain the final result in geographical coordinates (Figure 7a). The register- ing3.2. was D achieved reconstruction with ofa decimeter-level the Gazzano Cave accuracy (Table 7).

Table 7. TheSfM caveco-registering was surveyed errors. according to the proposed methodology (described in Section 2.4), validated as a result of the surveys in the tunnel. The entire image dataset was captured Targetsaccording RMSE to the X (m) following RMSE camera Y (m) settings: RMSE Z focal (m) length RMSE 18 XY mm; (m) iso 6400; Total exposureRMSE (m) time 1/1320 sec; f-number0.165 3.5 and 0.147 processed in 0.246 Agisoft Metashape 0.221 where the point 0.331 cloud was reconstructed. Prior to the calculation, the images were registered to the cave survey (FigureAs the2b) images’ to obtain quantity the final was result noteworthy in geographical (1597), coordinates the survey (Figure was processed7a). The registering in four separatewas achieved groups withand then a decimeter-level merged into a accuracy unique (Table3D model7). (Figure 7b). The use of image groups is vital to ease the processing of images and reduce the processing time. This is encouraging as the final output can represent the cave and its features.

Appl. Sci. 2021, 11, 6482 13 of 21 Appl. Sci. 2021, 11, x FOR PEER REVIEW 13 of 21

a)

b)

FigureFigure 7. 7.3D 3D model model of of the the cave cave withwith thethe locationslocations of control control points points (a (a) )and and camera camera shots shots (b ().b ).

Moreover, the SfM capability allows portraying the cave interior, too (Figure 8). This Table 7. SfM co-registering errors. feature can constitute the basis for realizing a low-resolution representation of the cave forTargets virtual exploration RMSE X (m) and RMSEtourism Y purposes. (m) RMSE The Z only (m) limit RMSE of the XY proposed (m) Total methodology RMSE (m) is the complete representation of the final chamber. The lighting and humidity conditions 20 0.165 0.147 0.246 0.221 0.331 did not illuminate its upper sector adequately, as it reaches 27 m (Figures 1 and 2a). A possible solution is adopting more acquisition points at different elevations inside the fi- As the images’ quantity was noteworthy (1597), the survey was processed in four nal chamber using cavers techniques to climb the wall and reach the top of the chamber. separate groups and then merged into a unique 3D model (Figure7b). The use of image The final result is a continuous model of the cave with a good resolution and accuracy. groups is vital to ease the processing of images and reduce the processing time. This is For traditional methods, the use of SfM allows the reconstruction of a 3D model without encouragingthe manual interpretation as the final output of cavers can that represent should thenot caveconnect and the its different features. benchmarks with the Moreover,manual representation the SfM capability of the cave. allows portraying the cave interior, too (Figure8). This feature can constitute the basis for realizing a low-resolution representation of the cave for virtual exploration and tourism purposes. The only limit of the proposed methodology is the complete representation of the final chamber. The lighting and humidity conditions did not illuminate its upper sector adequately, as it reaches 27 m (Figures1 and2a). A possible solution is adopting more acquisition points at different elevations inside the final chamber using cavers techniques to climb the wall and reach the top of the chamber. The final result is a continuous model of the cave with a good resolution and accuracy. For traditional methods, the use of SfM allows the reconstruction of a 3D model without the manual interpretation of cavers that should not connect the different benchmarks with the manual representation of the cave.

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Figure 8. Cave 3D model interior pictures. Figure 8. Cave 3D model interior pictures.

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3.3.3.3. High-Resolution High-Resolution Reconstruction Reconstruction of of an an Elemen Elementt of of Interest Interest for Virtual Tours Implementation NearNear the the final final chamber, chamber, an element of inte interestrest was noticed and used to focus on the experimentationexperimentation ofof aa high-resolution high-resolution survey survey (Figure (Figure9a). 9a). One One of the of mostthe most precious precious elements ele- mentsof the of Gazzano the Gazzano cave iscave the is presence the presence of a bigof a stalagmitebig stalagmite that that is located is located in thein the last last part part of ofthe the cave. cave. It It is is close close to to the the cave cave walls walls but but its its distancedistance fromfrom thethe cavecave wallwall is sufficientsufficient to capturecapture it it in in almost almost all all directions directions as schematized in Figure 99b.b. AccordingAccording toto thethe proposedproposed approach,approach, 92 92 photos photos of of the stalagmite were taken, following a circular path around the stalagmitestalagmite capturing capturing it it from from different different heights heights to to frame frame it it from from different different points points of of view. view. To To scalescale the the model, model, five five distances distances were were measured measured on on the the speleothem. speleothem. Natural Natural marks marks and andrec- ognizablerecognizable features features were were selected selected without without mark markinging the object the object with withthe purpose the purpose of its ofcom- its pletecomplete preservation. preservation.

a) b) c)

Figure 9. (a) photo of the stalagmite; (b) scheme of the high-resolution survey; (c) stalagmite 3D model. Figure 9. (a) photo of the stalagmite; (b) scheme of the high-resolution survey; (c) stalagmite 3D model.

TheThe imagery imagery was was then then processed processed in in Agisoft Agisoft Metashape Metashape as as in in the the previous previous experiment. experiment. TheThe distances distances were were inputted inputted in in the the processing processing procedures procedures by collimating by collimating the relative the relative fea- turesfeatures recognized recognized on the on photographs. the photographs. The scaling The scalingprocedure procedure provided provided an overall an centime- overall ter-levelcentimeter-level accuracy accuracy (0.0135 (0.0135m). The m). 3D Thepoint 3D cloud point was cloud further was further processed processed to compute to compute a 3D objecta 3D object (0.51 m (0.51 high m and high with and witha maximum a maximum diamet diameterer of 0.7 of m) 0.7 with m) withthe survey the survey picture picture mo- saickedmosaicked and and draped draped on it on (Figure it (Figure 9c).9 c).

4.4. Discussion Discussion ThisThis paper paper analyzed and compared the possible methodologies for acquiring a 3D modelmodel of of an an underground underground structure. The The reali realizationzation of three-dimensional point clouds of indoorindoor and and underground underground structures structures has has been been a a critical activity that now was facilitated thanksthanks to innovative solutions.solutions. InIn thethe previousprevious chapter, chapter, we we presented presented the the compared compared use use of ofTLS, TLS, PLS, PLS, and and SfM SfM on on the the same same test test site. site. The The PLS PLS is is aa recentlyrecently availableavailable solution,solution, with a lowerlower cost cost if if compared compared to to the the TLS. PLS can be adopted for the acquisition of indoor and undergroundunderground elements, elements, where where it it can can be be very very useful useful because because it it works works dynamically dynamically [41]. [41]. ThanksThanks toto thethe embeddedembedded sensors sensors and and processing processing algorithms, algorithms, these these sensors sensors can progressivelycan progres- sivelycover cover the entire the entire area of area interest of interest with awith progressive a progressive merging merging of a sequence of a sequence of surveys of surveys that thathave have overlapping overlapping sectors. sectors. The The PLS PLS software software uses uses common common sectors sectors to to linklink thethe different acquisitions.acquisitions. TLS TLS and and PLS PLS show show how how active active sensors sensors can canbe often be often considered considered the best the solu- best tionsolution because because they theydo not do have not have problems problems with withdifferent different conditions conditions of illumination of illumination that characterizethat characterize passive passive sensors sensors like likephoto photo came cameras.ras. On the On thecontrary, contrary, the thecombined combined use use of photoof photo cameras cameras and andstructure structure from from motion motion has ha hasd an had explosive an explosive diffusion diffusion in the inlast the years. last Thisyears. use This is often use issupported often supported by the growing by the growingdeployment deployment of RPAS in of the RPAS field in of the engineer- field of ingengineering geology [47] geology and [natural47] and hazards natural hazards[53], even [53 if], the even use if of the an use image of an sequence image sequence for the acquisitionfor the acquisition of a 3D model of a 3D based model on based SfM is on no SfMt exclusively is not exclusively linked to linkedthe use to of the RPAS. use In of manyRPAS. cases, In many this cases, approach this approach can be adopted can be adopted using different using different image image sequence sequence sources, sources, like like historical aerial images or terrestrial photos taken by smartphones or standard photo

Appl. Sci. 2021, 11, 6482 16 of 21

cameras [6,61]. The use of photo cameras becomes particularly useful if we have to consider the infrastructure’s price and the easiness of use. Table8 presents a brief comparison between the three categories of systems able to acquire a 3D point cloud considered in this paper.

Table 8. Characteristics of the analyzed instrumentations and peculiarities of the employed techniques.

Cost (Instrumentation) Cost (Software) Weight Manageability From ~3000 € to ~8000 TLS From ~30 k € to ~150 k € From ~5 kg to ~13 kg Low € for annual licenses (1) From ~30 k € to ~60 k € Embedded software From ~800 g to ~13 kg (comprehensive of multiple PLS providing real-time (e.g., backpack Medium sensors, i.e., LiDAR + MU + solutions solutions) DMI + Camera) From less than 100€ (e.g., From ~2500 € to ~4000 SfM webcams) to ~3000–4000 € From ~200 g to ~500 g High € for annual licenses (1) (e.g., professional cameras) Today there are free/open versions of processing software, although they cannot always guarantee the same usability (e.g., graphical interface) of commercial software. 4.1. TLS TLS represents, of course, the most powerful class of instruments. It represents the state-of-the-art for the acquisition of ultra-detailed 3D models [67]. From the commercial point of view, different instruments vary in terms of velocity of acquisition, resolution, and distance of acquisition and these characteristics affect the instrument cost (Table8). If we consider the static use of TLS, this category of instruments can be adopted, defining e sequence of scan positions that acquire part of the area of interest [9]. Post-processing allows creating a unique model from the union of different single scan position point clouds. As shown in Table8, in the underground condition, the high weight and the limited manageability of TLS can hamper their use. It becomes often impossible in natural caves, where the diameter of several parts of the cave can be too limited for the transport of heavy and voluminous materials. Long corridors very often characterize caves with a narrow diameter (even less than one meter) that connect wider chambers. In this context, the acquisition of a continuum 3D model based on the union of static scan positions is rarely the best solution. The effort for the setup of a scan position is not rewarded by the limited portion of the cave that the TLS can acquire. That means that having to ensure sufficient overlap between the scans to generate a complete model of the environment [9], the number of scan positions should be incredibly high. The effort for the acquisition of a long cave makes the use of TLS often not the right choice. Besides, the proper functioning of these instruments can also be influenced by the severe conditions of the environment [6]: the presence of high levels of humidity and consequent reflective surfaces can create electric problems to the instruments and reduce the reflectivity of cave faces (as the performance of the device). Under such conditions, the resulting point cloud could be affected by high noise and localized representation errors, which can be easily identified and eliminated, allowing proper scans registration [67].

4.2. PLS PLS were developed for dynamic use in indoor spaces [37]. This class of instruments evidenced its interesting potential in the survey of the tunnel segment. The limited range of the instruments is compatible with the tunnel section’s size and the acquisition of a sequence of close paths allows the progressive exploration and acquisition of the studied area. The main advantage given by the use of this technology, to the others (Table8), is the real-time generation of complete point clouds describing the surveyed environment, which takes place using mapping and registration algorithms integrated into the acquisition system. One of the critical points of this system is the continuity of the survey that is Appl. Sci. 2021, 11, 6482 17 of 21

required for a good result. This continuity could be guaranteed in a tunnel, but it is challenging to assure in a natural cave, where some corridors are very narrow and difficult to cross. The best practices in the use of PLS highlight the need to retrace at least part of the path several times to optimize the registration, taking advantage of the matching of common features between the clouds [66]. Additionally, it is recommended to start and finish the acquisition at the same point, to exploit loop closure algorithms to correct global drift errors [68]. Again, these operations may not be feasible in these complex natural environments. The operator’s experience in handling and managing the instrument also has a strong influence on the acquisition process’s final result [41]. It is necessary to avoid moving the instrument abruptly or to make specific movements that can negatively affect the attitude estimation and data processing algorithms, such as vertical movements, rotating the instrumental origin or turn, and tilt at the same time [69]. Often, acquisitions too close to surfaces (<30–50 cm) can create problems in the point clouds registration phase, as can happen in tight quarters. The active sensor of PLS is an important feature of this class of instruments that can be vulnerable in natural caves where the level of humidity is very high. As described for TLS, the moisture can hamper the use of PLS because the water on the sides of the cave reduced the reflectivity of the material, and, with intensive use, it can damage the instruments. In general, these environmental conditions are unfavorable for all active sensors. Unlike the individual scans acquired with TLS, it is impossible to manually remove any errors in PLS products. This limitation increases the noise of the final point cloud generation and could also create some registration errors [67]. These critical errors cannot be easily removed even with post-processing software. As demonstrated in this work, the point clouds generated with PLS technology are significantly more noisy and less dense than those obtained with terrestrial laser scanners; several works [37–39] show that it would be appropriate to travel the same trajectory at least twice, the first by limiting the tilt of the instrument, to create a reference map, the second with more freedom of movement to densify the data and acquire all the information that was ignored at the first step (such as the high parts of the walls and ceilings).

4.3. SfM The use of a photo camera is, of course, the cheapest and most straightforward approach as reported in Table8. In chapter three, we presented the developed methodology to acquire a sequence of images in a cave that can have sufficient overlap to assure the correct use of SfM algorithms. The analysis of results obtained in the tunnel test site pointed out that the structure from motion’s accuracy can be very high and not far from the PLS. The use of an image sequence has a double advantage: it is easy to obtain and it can be adopted not only for the reconstruction of the morphology of the caves but also for the rendering of most precious elements. In the Gazzano case study, we presented two different modalities of use: (i) the acquisition of a photo sequence characterized by the minimum required overlap adopted for the reconstruction of the geometry of the cave; (ii) an ultra-detailed acquisition of a photo sequence of a particular element of the cave (the stalagmite) for the reconstruction of a solid image. These different approaches allow obtaining results with distinct characteristics and final use. The use of a photo camera has another significant added value: its high compatibility with the actual approach adopted by many speleologists for caves survey. As presented initially, the adopted method is based on the reconstruction of a sequence of measures done with a disto-clinometer that creates a polygonal. The series of points of the polygonal allows the description of the cave using a discrete approach. The main limitation of this approach is related to the use of punctual measures that cannot continuously describe the cave’s morphology. The SfM can close this gap: the points acquired by the disto-clinometer can be used as ground control points that are fundamental both for a correct reconstruction of the geometry of the cave with a photo sequence and to scale the model, because, unlike the results obtained with active sensors, the photogrammetric models are not scaled. The impact of the method is minimal. The only Appl. Sci. 2021, 11, 6482 18 of 21

sign of the survey is a sequence of small dots (few millimeters) representing the polygonal nodes. That can be identified in the high-resolution images acquired by the camera. From the point of view of the final product density, the photogrammetric point clouds are comparable with those made with TLS technology, although a bit noisier. The noise effect may be due to some inaccuracy in the images’ alignment or to the instability of passive sensors, especially those at low cost. In the case of underground caves, these problems are related to the environments’ poor lighting or the light variation, with reflective surfaces and shadows, which do not allow to identify the matching points between the images.

5. Conclusions The establishment of new technologies, instruments, and algorithms for the execution and processing of three-dimensional surveys have expanded the possibility of performing such surveys in underground environments, like caves. The traditional approach was based on a series of reference points along the cave measured by clinometer and compass and led to a discrete representation of the cave. This method is currently improved by using laser portable range finders, which allow faster execution of measures and their storage in built-in memory or in an app. The methodology proposed and tested in the work is aimed to achieve a full three-dimensional reconstruction of the cave while assuring its accuracy in terms of positioning. To test the methodology, before the cave survey, a benchmark site was surveyed. A stretch of a tunnel was surveyed by TLS, PLS, and camera shooting. All the three-point clouds were registered to the reference targets with centimeter-level accuracy, i.e., 0.015, 0.010, and 0.012 m, respectively, allowing the following phases of the research. The tests provided that the point clouds obtained by PLS and by SfM processing of the shooting are in good agreement with the TLS one, selected as the reference data set. PLS-TLS comparison provided an average residual of 0.053 m while SfM-TLS 0.040 m. The tunnel was also selected as a test site thanks to the easy accessibility, which allowed to employ of bulky instrumentation. According to the test site result, we developed a methodology to survey natural caves adopting only a photo camera and SfM processing. The proposed methodology is tested in the Gazzano cave, located in the Garessio municipality (Piemonte region, northwestern Italy). The cave is 105 m long, and it has several chambers, with a diameter that ranges from 37 m up to less than one meter. The cave was provided with several reference points surveyed by the cavers that were used as ground control points. According to the proposed methodology, the image acquisition was carried out in both directions from the entrance to the end of the cave and backward. The SfM processing provided the point cloud reconstructing the entire cave except for the final chamber whose ceiling was 25-m-high and images were not enough light. During the survey, a peculiar stalagmite was surveyed, too. This survey aimed to reconstruct a single object with a higher resolution if compared to the whole cave model. The reconstruction of this speleothem allowed to confirm the validity of the proposed method. The registering of the whole cave was carried out with an overall accuracy of 0.331 m; the scaling accuracy of the speleothem reached an overall accuracy of 0.0135 m. By analyzing the benchmark and the cave survey, it was also possible to summarize the different survey methods’ pros and cons in underground contexts. Namely, SfM resulted in the most manageable and affordable, both in terms of equipment and processing software. On the contrary, PLS and, particularly, TLS are expensive and with less maneuverability. A possible solution, for cost reduction, is the use of available open-source alternatives of processing software which can, even if often lacking user-friendly interfaces, provide similar functionalities.

Author Contributions: Conceptualization, D.G. (Daniele Giordan) and M.P.; data curation, N.G.and R.Z.; investigation, D.G. (Danilo Godone), M.B., N.G.and R.Z.; methodology, D.G. (Daniele Giordan), D.G. (Danilo Godone), M.B. and R.Z.; supervision, M.P.; validation, M.B.; writing—original draft, D.G. (Daniele Giordan), D.G. (Danilo Godone) and N.G.; writing—review and editing, M.P. All authors have read and agreed to the published version of the manuscript. Appl. Sci. 2021, 11, 6482 19 of 21

Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors thank the Speleo Club Tanaro for the logistic support. Conflicts of Interest: The authors declare no conflict of interest.

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