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Self-Organized Model Fitting Method for Railway Structures Monitoring Using Lidar Point Cloud

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Self-Organized Model Fitting Method for Railway Structures Monitoring Using Lidar Point Cloud remote sensing Article Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud Amila Karunathilake * , Ryohei Honma and Yasuhito Niina Advanced Technologies Research Laboratory, Asia Air Survey Co. Ltd, 1-2-2 Manpukuji, Asao-ku, Kawasaki-shi 215-0004, Kanagawa Prefecture, Japan; [email protected] (R.H.); [email protected] (Y.N.) * Correspondence: [email protected] Received: 12 October 2020; Accepted: 9 November 2020; Published: 11 November 2020 Abstract: Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring. Keywords: mobile laser scanning (MLS); railway infrastructure monitoring; model fitting; 3D point cloud 1. Introduction The mobile laser scanning (MLS) point cloud becomes a more accurate and sophisticated technique to acquire terrestrial information from the surrounding environment. Currently, it has become a more popular method to monitor the quality and the safety of the social infrastructure systems such as railway roads [1], highways and public roads [2,3], bridges and other peripherals [4–8]. During the past few years, maintenance of public transportation, especially, railway track monitoring by MLS has been widely used in many institutions due to its advantage of providing precise estimation in railway structures up to the millimeter level accuracy [9,10]. Figure1 depicts the 3D models generated by MLS survey to monitor the railway infrastructure and surrounding environment, which is very important and coequal to the physical inspections for railway maintenance. The structures in (a), (b) and (c) Remote Sens. 2020, 12, 3702; doi:10.3390/rs12223702 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, x FOR PEER REVIEW 2 of 16 Remote Sens. 2020, 12, 3702 2 of 16 environment, which is very important and coequal to the physical inspections for railway maintenance. The structures in (a), (b) and (c) depict the ridged metal structures, non-ridged metal depictstructures the ridgedsuch as metal electric structures, wires and non-ridged non-metal metal equipment structures, which such are as located electric above wires the and human non-metal eye equipment,contacting range. which Some are located of those above locations the human can be eye accessible contacting for range. inspection, Some ofbut those some locations of them can need be accessiblespecially equipped for inspection, machinery but some such of as them cranes need to speciallytake the measurements. equipped machinery Figure such 1d depicts as cranes a railway to take thetrack measurements. passing through Figure the1 dconcrete depicts atunnel railway, which track seems passing monotonous through the for concrete low resolution. tunnel, which However, seems monotonoushigher point fordensity low resolution.of the 3D point However, cloud higher inside point the tunnel density surface of the 3Dpermitted point cloud to capture inside thestructural tunnel surfacedefects of, permitted not only to the capture railway structural infrastructure, defects of,but notalso only the thesurrounding railway infrastructure, environment. butVisualizing also the surroundingsuch information environment. in the GIS Visualizingplatform [11 such,12] increases information the inefficiency the GIS of platform infrastructure [11,12] increasesmanagement the eandfficiency decision of infrastructure making. Minimum management workforce, and decisionlong observation making. Minimum within a short workforce, period, long day observation and night withintime monitoring a short period, process day and and the night possibility time monitoring of keeping process a track and history the possibility for further of analysis keeping become a track historyfavorable for factors further of analysis MLS becomebased survey favorable for factorsrailway of maintenance MLS based survey companies for railway and their maintenance regular companiesmaintenance and work. their Therefor regulare maintenancerailway structure work. monitoring Therefore by railway MLS becomes structure the monitoring most reliable by MLS and becomescost effective the most method reliable accepted and costby many effective countries method for accepted both private by many and countriesgovernment for operated both private railway and governmentsystems [13– operated15]. railway systems [13–15]. (a) (b) (c) (d) (e) (f) (g) (h) (i) Figure 1. Point cloud acquired near to the railway:railway: ( a) station platform (b) bridge (c) catenary system ((d)) concreteconcrete tunnel tunnel (e ()e track) track bed bed in flatin flat terrain terrain (f) track (f) track bed inbed slope in terrainslope terrain (g) level (g crossing) level crossing (h) turnout (h) (tiurnout) multipel (i) multipel trackes during trackes the during mobile the laser mobile scanning laser scanning (MLS) survey. (MLS) survey. However, duedue to experimentalto experimental defects defects during during the MLS the survey MLS such survey as poor such sampling, as poor misalignment sampling, ofmisalignment the data acquisition of the data system, acquisition perturbations system, in GPSperturbations and limitations in GPS in the and data limitations processing in algorithm, the data theprocessing accurate algorithm, estimation the of 3Daccurate terrestrial estimation structures, of 3D their terrestrial dimensions structures, and special their locationsdimensions become and morespecial challenging locations become [16,17]. more The inside challenging corners [1 of6,1 the7]. The rail headinside is corners defined of as the the rail railway head gaugeis defined corner as (hereinafter,the railway gauge this is corner abbreviated (hereinafter, to “GC”), this is which abbreviated becomes to an“GC”) important, which position becomes for an pointimportant data alignmentposition for [1 point]. The data precise alignment estimation [1]. The of railway precise infrastructureestimation of railway such as infrastructure the rail track depends such as the on rail the accuratetrack depends estimation on the of accurate the railway estimation GC. The of point the cloudrailway captured GC. The from point the cloud ideal captured railway track from bed the in ideal flat terrainrailway is track depicted bed in in flat Figure terrain1e. In is thisdepicted scenario, in Figure both left1e. andIn this right scenario, rail track both positions left and are right in therailsame track elevation.positions are Therefore in the same the distributionelevation. Therefore of point cloudthe distribution near GC in of both point the cloud left andnear right GC in of both the railway the left trackand right has similaritiesof the railway in horizontal track has andsimilarities zero altitude in horizontal change inand the zero vertical altitude plane. change However, in the Figure vertical1f Remote Sens. 2020, 12, 3702 3 of 16 depicts the railway track running on a slope surface. In this scenario, the distribution of point cloud near GC in left or right or both railway tracks are varying in horizontal and vertical planes. Moreover, railway crossings, turnouts and combined locations depicted in Figure1g–i make the GC extraction process more complicated than the ideal location in (e). This can be identified as one of the major limitations for accurate estimation of GC. This affects specially for long distance MLS survey in the urban environment, which contains
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