Localization of a mobile laser scanner via dimensional reduction

We extend the concept of intrinsic localization from a theoretical one-dimensional (1D) solution onto a 2D manifold that is embedded in a 3D space, and then recover the full six degrees of freedom for a mobile laser scanner with a simultaneous localization and mapping algorithm (SLAM). By intrinsic localization, we mean that no reference coordinate system, such as global navigation satellite system (GNSS), nor inertial measurement unit (IMU) are used. Experiments are conducted with a 2D laser scanner mounted on a rolling prototype platform, VILMA. The concept offers potential in being extendable to other wheeled platforms.

[1]  Thierry Peynot,et al.  Error modeling and calibration of exteroceptive sensors for accurate mapping applications , 2010 .

[2]  Davide Scaramuzza,et al.  Continuous-Time Trajectory Estimation for Event-based Vision Sensors , 2015, Robotics: Science and Systems.

[3]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Harri Kaartinen,et al.  Localization of mobile laser scanner using classical mechanics , 2015 .

[5]  Michael Bosse,et al.  Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping , 2012, IEEE Transactions on Robotics.

[6]  Klaus Schilling,et al.  Evaluation of a Backpack-Mounted 3D Mobile Scanning System , 2015, Remote. Sens..

[7]  Moustafa Youssef,et al.  Indoor Localization , 2017, Encyclopedia of GIS.

[8]  Thierry Peynot,et al.  Error modeling and calibration of exteroceptive sensors for accurate mapping applications , 2010, J. Field Robotics.

[9]  Tim D. Barfoot,et al.  Towards relative continuous-time SLAM , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Peter Schneider,et al.  Study of parameterizations for the rigid body transformations of the scan registration problem , 2010, Comput. Vis. Image Underst..

[11]  Sebastian Thrun,et al.  FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics , 2007 .

[12]  Achim J. Lilienthal,et al.  Maximum likelihood point cloud acquisition from a mobile platform , 2009, 2009 International Conference on Advanced Robotics.

[13]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[14]  George Vosselman.  Design of an indoor mapping system using three 2D laser scanners and 6 DOF SLAM , 2014 .

[15]  Sebastian Thrun,et al.  The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures , 2006, Int. J. Robotics Res..

[16]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Paul Newman,et al.  Self-calibration for a 3D laser , 2012, Int. J. Robotics Res..

[18]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[19]  Andreas Nüchter,et al.  Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms , 2013, Remote. Sens..

[20]  Tim D. Barfoot,et al.  Relative continuous-time SLAM , 2015, Int. J. Robotics Res..

[21]  Michael Bosse,et al.  Continuous 3D scan-matching with a spinning 2D laser , 2009, 2009 IEEE International Conference on Robotics and Automation.

[22]  Avideh Zakhor,et al.  Indoor localization and visualization using a human-operated backpack system , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[23]  Martin Pfennigbauer,et al.  Boresight alignment method for mobile laser scanning systems , 2010 .

[24]  Andreas Nüchter,et al.  Localization corrections for mobile laser scanner using local support-based outlier filtering , 2016 .

[25]  Derek D. Lichti,et al.  Rigorous approach to bore-sight self-calibration in airborne laser scanning , 2006 .

[26]  Frank Dellaert,et al.  A hierarchical wavelet decomposition for continuous-time SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[27]  Hugh F. Durrant-Whyte,et al.  A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[28]  Joachim Hertzberg,et al.  Globally consistent 3D mapping with scan matching , 2008, Robotics Auton. Syst..

[29]  J. Kostamovaara,et al.  On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques , 2015, IEEE Photonics Journal.

[30]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1997, J. Intell. Robotic Syst..

[31]  Timothy A. Davis,et al.  Algorithm 8 xx : a concise sparse Cholesky factorization package , 2004 .

[32]  Michael Bosse,et al.  Continuous‐Time Three‐Dimensional Mapping for Micro Aerial Vehicles with a Passively Actuated Rotating Laser Scanner , 2016, J. Field Robotics.

[33]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[34]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[35]  Richard Szeliski,et al.  Vision Algorithms: Theory and Practice , 2002, Lecture Notes in Computer Science.

[36]  Brett Browning,et al.  Continuous trajectory estimation for 3D SLAM from actuated lidar , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[37]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.