Towards LIDAR-RADAR based terrain mapping

This paper addresses the problem of perception for autonomous vehicle navigation in real environments. Integrity safe navigation of autonomous vehicles in unknown environments poses a traversability problem. We are interested in the integrity-safe navigation in unknown environments. Safe navigation is a task that depends on the knowledge of the surrounding environment and the vehicle dynamics. Classical navigation approach focus on obstacle avoidance often based on occupancy and elevation maps. We propose to combine an optical sensor and an electromagnetic sensor to build a richer map of the environment which will be used for traversability analysis and path planning. The proposed lidar-radar map encodes the geometry of the environment such that traversability analysis and trajectory planning guarantee the robot's integrity in a stability sense. A comparative analysis of two mapping algorithms using lidar, radar, IMU and GPS sensors shows the advantages of such bimodal perception system. Results have been validated experimentally.

[1]  Robert E. Mahony,et al.  Nonlinear Complementary Filters on the Special Orthogonal Group , 2008, IEEE Transactions on Automatic Control.

[2]  G. Sirakoulis,et al.  Stereo-based terrain traversability analysis for robot navigation , 2009 .

[3]  Alberto Broggi,et al.  Terrain mapping for off-road Autonomous Ground Vehicles using rational B-Spline surfaces and stereo vision , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[4]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[5]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[6]  Marc Levoy,et al.  Geometrically stable sampling for the ICP algorithm , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[7]  Raphaël Rouveure,et al.  Methods for FMCW radar map georeferencing , 2013 .

[8]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[9]  Marc Levoy,et al.  The digital Michelangelo project: 3D scanning of large statues , 2000, SIGGRAPH.

[10]  Thierry Peynot,et al.  The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions , 2010, Int. J. Robotics Res..

[11]  Viii Supervisor Sonar-Based Real-World Mapping and Navigation , 2001 .

[12]  Roland Siegwart,et al.  Long-term 3D map maintenance in dynamic environments , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[13]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[14]  M.O. Monod,et al.  Mapping of the environment with a high resolution ground-based radar imager , 2008, MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference.

[15]  Luiz Marcos Garcia Gonçalves,et al.  Probabilistic robotic grid mapping based on occupancy and elevation information , 2013, 2013 16th International Conference on Advanced Robotics (ICAR).

[16]  Christophe Debain,et al.  Digital Elevation Map estimation by vision-lidar fusion , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[17]  M. Trivedi,et al.  Off-Road Terrain Traversability Analysis and Hazard Avoidance for UGVs , 2011 .

[18]  Philippe Martinet,et al.  Automatic guidance of a farm tractor along curved paths, using a unique CP-DGPS , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[19]  Simon Lacroix,et al.  Digital elevation map building from low altitude stereo imagery , 2002, Robotics Auton. Syst..

[20]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.