An Adaptive Camera and Lidar Joint Calibration Algorithm

In large mechanism intelligent control system, cameras and lidars are usually mounted to obtain environmental information. In order to fuse the information of camera and lidar, joint calibration is essential to place their data under the same coordinate system. To solve the problem of camera and 3D lidar joint calibration, an adaptive joint calibration method is proposed. Only one planar calibration board is needed for the calibration. In the nonlinear optimization procedure, the camera extrinsic parameters and optimized together with the rotation matrix and translation vector from lidar to camera coordinate. Compared with the method of fixed camera parameters, average calibration error decreases by 22.50%. By using weighed lidar data, calibration error further decreases by 8.80% in average.

[1]  Roland Siegwart,et al.  Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Vincent Frémont,et al.  Extrinsic calibration between a multi-layer lidar and a camera , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[3]  Olivier Strauss,et al.  Calibration of a multi-sensor system laser rangefinder/camera , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[4]  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.

[5]  Olli Jokinen.  Self-calibration of a light striping system by matching multiple 3-D profile maps , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[6]  Huijing Zhao,et al.  An efficient extrinsic calibration of a multiple laser scanners and cameras' sensor system on a mobile platform , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[7]  Antonis A. Argyros,et al.  Fusion of laser and visual data for robot motion planning and collision avoidance , 2003, Machine Vision and Applications.

[8]  Lili Huang,et al.  A novel multi-planar LIDAR and computer vision calibration procedure using 2D patterns for automated navigation , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[9]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[10]  Yoshihiko Nakamura,et al.  Laser-pointing endoscope system for intra-operative 3D geometric registration , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[11]  Salvatore Strano,et al.  A method for the calibration of a 3-D laser scanner , 2011 .

[12]  Yunhui Liu,et al.  An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  M. Mahlisch,et al.  Sensorfusion Using Spatio-Temporal Aligned Video and Lidar for Improved Vehicle Detection , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[14]  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).

[15]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .