Extrinsic 6DoF calibration of 3D LiDAR and radar

Environment perception is a key component of any autonomous system and is often based on a heterogeneous set of sensors and fusion thereof, for which extrinsic sensor calibration plays fundamental role. In this paper, we tackle the problem of 3D LiDAR-radar calibration which is challenging due to low accuracy and sparse informativeness of the radar measurements. We propose a complementary calibration target design suitable for both sensors, thus enabling a simple, yet reliable calibration procedure. The calibration method is composed of correspondence registration and a two-step optimization. The first step, reprojection error based optimization, provides initial estimate of the calibration parameters, while the second step, field of view optimization, uses additional information from the radar cross section measurements and the nominal field of view to refine the parameters. In the end, results of the experiments validated the proposed method and demonstrated how the two steps combined provide an improved estimate of extrinsic calibration parameters.

[1]  Gabe Sibley,et al.  Online SLAM with any-time self-calibration and automatic change detection , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Hans-Joachim Wünsche,et al.  Odometry-based online extrinsic sensor calibration , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Sebastian Thrun,et al.  Automatic Online Calibration of Cameras and Lasers , 2013, Robotics: Science and Systems.

[4]  C. G. Stephanis,et al.  Trihedral rectangular ultrasonic reflector for distance measurements , 1995 .

[5]  Takeo Kanade,et al.  Extrinsic calibration of a single line scanning lidar and a camera , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  S. Sugimoto,et al.  Obstacle detection using millimeter-wave radar and its visualization on image sequence , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[7]  Nanning Zheng,et al.  Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications , 2011, Sensors.

[8]  Kostas Daniilidis,et al.  MSG-cal: Multi-sensor graph-based calibration , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Zhidong Deng,et al.  Extrinsic calibration of a camera and a lidar based on decoupling the rotation from the translation , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[10]  Silvio Savarese,et al.  Extrinsic Calibration of a 3D Laser Scanner and an Omnidirectional Camera , 2010 .

[11]  Andreas Nüchter,et al.  Mutual Calibration for 3D Thermal Mapping , 2012, SyRoCo.

[12]  Andreas Geiger,et al.  Automatic camera and range sensor calibration using a single shot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[13]  Robert Pless,et al.  Extrinsic calibration of a camera and laser range finder (improves camera calibration) , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[14]  Dimitrios G. Kottas,et al.  3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization , 2012, Int. J. Robotics Res..

[15]  Thierry Peynot,et al.  Characterisation of the Delphi Electronically Scanning Radar for robotics applications , 2015, ICRA 2015.

[16]  Adam Herout,et al.  Calibration of RGB camera with velodyne LiDAR , 2014 .

[17]  Eugene F. Knott,et al.  Radar Cross Section Measurements , 1993 .