Extrinsic Calibration for Lidar and Stereo Vision Using 3D Feature Points

被引:2
|
作者
Chen Shaojie [1 ,2 ]
Zhu Zhencai [3 ]
Zhang Yonghe [1 ]
Guo Ming [1 ]
Zhi Shuai [1 ]
机构
[1] Chinese Acad Sci, Innovat Acad Microsatellite, Shanghai 201203, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] CAS Key Lab, Innovat Acad Microsatellites, Shanghai 201203, Peoples R China
关键词
atmospheric optics; lidar; stereo camera; calibration parameter; rigid-body transformation;
D O I
10.3788/LOP57.030102
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Lidar and stereo cameras arc important environmental sensors for unmanned driving. Calibrating external parameters between these two sensors is an important basis for their combination; however, combining two types of information requires a complex calibration process. This paper proposes a method based on feature point pair matching. Two rectangular planks arc used to extract the 3D point cloud of the edge of the board in stereo vision and lidar coordinate systems, which is then used to obtain the corner coordinates. Finally, the Kabsch algorithm is used to solve the coordinate transformation between the paired feature points, and a clustering method is used to remove outliers from the multiple measurements and obtain the average value. By setting up an experiment, this method can be implemented on the Nvidia Jetson Tx2 embedded development board, and accurate registration parameters can be obtained, thus verifying the theoretical method's feasibility. This registration method is simple and easy to execute, can automatically perform multiple measurements, and is improved compared with similar methods.
引用
收藏
页数:8
相关论文
共 17 条
  • [1] [Anonymous], 2013, 2013 IEEE INT C ROB
  • [2] [Anonymous], 2013, Robot.: Sci. Syst.
  • [3] [Anonymous], 2017, CHINESE J LASERS, DOI DOI 10.1088/1361-6471/AA8DAA
  • [4] Bok Y, 2013, IEEE INT CONF ROBOT, P2880, DOI 10.1109/ICRA.2013.6630976
  • [5] Castorena J, 2016, INT CONF ACOUST SPEE, P2862, DOI 10.1109/ICASSP.2016.7472200
  • [6] RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY
    FISCHLER, MA
    BOLLES, RC
    [J]. COMMUNICATIONS OF THE ACM, 1981, 24 (06) : 381 - 395
  • [7] Geiger A, 2012, IEEE INT CONF ROBOT, P3936, DOI 10.1109/ICRA.2012.6224570
  • [8] Guindel C, 2017, IEEE INT C INTELL TR
  • [9] Hirschmüller H, 2008, IEEE T PATTERN ANAL, V30, P328, DOI 10.1109/TPAMl.2007.1166
  • [10] John V, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON VEHICULAR ELECTRONICS AND SAFETY (ICVES), P231, DOI 10.1109/ICVES.2015.7396923