School of Engineering and Science Dissertation for a Ph.D. in Computer Science Improving Point Cloud Quality for Mobile Laser Scanning Jan Elseberg November 2013 Supervisor: Prof. Dr. Andreas Nüchter Second Reviewer: Prof. Dr. Andreas Birk Third Reviewer: Prof. Dr. Joachim Hertzberg Fourth Reviewer: Prof. Dr. Rolf Lakämper Date of Defense: 13th of September 2013 Abstract This thesis deals with mobile laser scanning and the complex challenges that it poses to data processing, calibration and registration. New approaches to storing, searching and displaying point cloud data as well as algorithms for calibrating mobile laser scanners and registering laser scans are presented and discussed. Novel methods are tested on state of the art mobile laser scanning systems and are examined in detail. Irma3D, an autonomous mobile laser scanning platform has been developed for the purpose of experimentation. This work is the result of several years of research in robotics and laser scanning. It is the accumulation of many journal articles and conference papers that have been reviewed by peers in the field of computer science, robotics, artificial intelligence and surveying. Danksagung The work that goes into finishing a work like this is long and arduous, yet it can at times be pleasing, exciting and even fun. I would like to thank my advisor Prof. Dr. Andreas Nüchter for providing such joy during my experiences as a Ph.D. student. His infectious eagerness for the subject and his ability to teach are without equal. Prof. Dr. Joachim Hertzberg deserves many thanks for introducing me to robotics and for being such an insightful teacher of interesting subjects. Ich danke zudem meinen Eltern, deren Unterstützung unschätzbaren Wert hat, meinen Geschwister, Niels, Tim und Tobias, meiner brillianten Kollegin und besten Freundin Dorit Borrmann, die mir immer mit Rat und Tat zur Seite stand, und allen meinen anderen Freunden, die es ebenfalls verdient haben, erwähnt zu werden. Aus tiefstem Herzen vielen Dank! v Contents 1 Introduction 1 1.1 Applications of Laser Scanning . 4 1.2 Laser Scanners and Other Range Sensors . 10 2 Rigid registration for terrestrial laser scanning 19 2.1 Registration based on Artificial Landmarks . 22 2.2 Automatic Registration . 23 2.2.1 Globally Consistent 3D Mapping with Scan Matching . 25 2.2.2 Study of Parameterizations for the Rigid Body Transformations of the Scan Registration Problem . 53 2.3 Point Cloud Processing . 99 2.3.1 An Octree for Efficient Processing of 3D Laser Scans . 99 2.3.2 Comparison of Nearest-Neighbor-Search Strategies and Implementations for Efficient Shape Registration . 135 3 Mobile Laser Scanning 155 3.1 Irma3D . 158 3.2 Algorithmic Solutions for computing accurate maximum likelihood 3D Point Clouds from Mobile Laser Scanning Platforms . 165 4 Conclusion 205 vii Chapter 1 Introduction We shall not cease from exploration And the end of all our exploring Will be to arrive where we started And know the place for the first time. T.S. Eliot, Little Gidding The science of surveying, the art of measuring space, the quantification of distances and angles are all ancient dating back to at least the old kingdom of Egypt. As early as 2540 BC the Egyptians were capable of constructing almost perfectly aligned and proportioned Pyramids using only simple tools and basic geometry [43]. Since then many advances have been made in these fields. From the classical Dioptra over the Azimuthal Quadrant to the modern Theodo- lite, surveyors developed ever more accurate and powerful tools (Fig. 1.1). Concurrent with the increasing complexity of the equipment and the increasing number of applications, advanced mathematical methods were also developed simultaneously. For example, the method of trian- gulation, which is still used to this day, was first described by Gemma Frisius in 1533 [32]. More often than not mathematical advances preceded or encouraged the development of technologies. The solution to the problem of longitude, that is, the task of determining the longitudinal co- ordinate of a point at sea, was well known in theory many decades before it could be solved in practice, by John Harrison when he invented the marine chronometer [68]. In 1997 one of the most versatile and powerful tools for surveying, the laser scanner, appeared on the market. A laser scanner, essentially a combination of a theodolite and a laser range finder, is capable of taking several thousands of range and angle measurements a second. The ability to rapidly acquire spatial data has led to a surge of new developments not only in the field of surveying but also in related fields such as robotics and computer science. As is frequently the case in computer science, this new technology has revealed a multitude of computational problems that are in need of algorithmic solutions. This thesis is the result of several years of research and of investigating solutions to the challenges that have newly emerged. During this time these new measurement devices were used for a variety of different applications. The most straight-forward use of a laser scanner is certainly as a surveying tool. First, one acquires several views on whatever the object, building or environment to be surveyed. Then one assembles a map, using either classical surveying 1 2 CHAPTER 1. INTRODUCTION Fig. 1.1: Top from left to right: A Dioptra, an Azimuthal Quadrant and a theodolite. All these devices are used for angle measurements of points relative to the observer. The Dioptra is an ancient tool that was invented at least 2000 years ago [1]. In the 13th century the Persian scholar Nasir al-Din al-Tusi invented the Azimuthal Quadrant for observing the movement of celestial bodies [41]. The theodolite was invented in the 16th century by Martin Waldseemüller who called it the polimetrum [2]. Since then many improvements to the original design have been made, resulting in modern theodolites such as the Topcon DT-205 (bottom left) [16]. The combination of a range finder with these devices is embodied by the tachymeter (bottom middle). The Cyrax 2400 (bottom right) is one of the first laser scanners to appear on the market in 1998 [27]. Laser scanners can be considered an advanced development of the tachymeter, in the sense that they acquire the same measurements, only at a much higher rate and do not require special targets. Images taken from the manufacturers. techniques or automatic algorithmic solutions. Automating this process further, by fixing the laser scanner on a mobile platform, creates yet again new opportunities and new challenges. Recently, this has culminated in a new process called mobile laser scanning, where the laser scanner is in use while the mobile platform is in motion. The opportunities inherent in this mode Improving Point Cloud Quality for Mobile Laser Scanning 3 of operation are obvious for robotics. The laser scanner is transformed from a surveying tool into a sensor valuable to the robot itself. The laser scanner enables a new mode of experiencing the environment that other sensors either cannot provide to such precision or only provide indirectly. Although a laser scanner gives a purely metrical impression of the world, machine learning techniques can be used to extract additional semantic information out of the data as well. The main focus has been to develop methods for creating accurate digital models of the world by using spatial information that is captured by laser scanners. These algorithms ought to require the least amount of human intervention as possible, ideally none at all, to the main benefit of automating and therefore speeding up these processes. The resulting model of the world is supposed to bea spatial likeness of the object or scene that has been captured. For this, one strives to create accurate, precise, yet compact representations of the data. Combining these should not only be possible, but easy, so as to create more accurate and more detailed representations of the world. This thesis is an accumulation of my work done on the topic of laser scanning in the format of a collection of journal articles. The papers herein have been peer-reviewed by an international community of scientists and experts in the field of computer science, robotics, artificial intelligence or surveying or a combination thereof. All of the following documents have therefore appeared in other publications before. Manuscripts that appear in this work: D. Borrmann, J. Elseberg, K. Lingemann, A. Nüchter, and J. Hertzberg. Globally Consistent 3D Mapping with Scan Matching. Journal of Robotics and Autonomous Systems (JRAS), 56(2):130– 142, February 2008. http://www.journals.elsevier.com/robotics-and-autonomous-systems J. Elseberg, D. Borrmann, and A. Nüchter. Algorithmic solutions for computing precise maxi- mum likelihood 3D point clouds from mobile laser scanning platforms. Remote Sensing (accepted) , 2013. http://www.mdpi.com/journal/remotesensing J. Elseberg, D. Borrmann, and A. Nüchter. One Billion Points in the Cloud - An Octree for Efficient Processing of 3D Laser Scans. ISPRS Journal of Photogrammetry and Remote Sensing, 76(0):76–88, 2013. http://www.journals.elsevier.com/isprs-journal-of-photogrammetry- and-remote-sensing J. Elseberg, S. Magnenat, R. Siegwart, and A. Nüchter. Comparison of Nearest-Neighbor- Search Strategies and Implementations for Efficient Shape Registration. Journal of Software Engineering for Robotics (JOSER), 3(1):2 – 12, 2012. http://www.joser.org A. Nüchter, J. Elseberg, P. Schneider, and D. Paulus. Study of Parameterizations for the Rigid Body Transformations of The Scan Registration Problem. Journal Computer Vision and Image Understanding (CVIU), 114(8):963–980, August 2010. http://www.journals.elsevier. com/computer-vision-and-image-understanding Improving Point Cloud Quality for Mobile Laser Scanning 4 CHAPTER 1. INTRODUCTION Fig. 1.2: Two green laser beams are pointed at the moon by the Laser Ranging Facility at the Geophys- ical and Astronomical Observatory at NASA’s Goddard Space Flight Center in Greenbelt, Md.