Extrinsic calibration of stereo camera and three-dimensional laser scanner
Abramenko A.A.


Joint Stock Company "Scientific Design Bureau of Computing Systems" (JSC SDB CS), Taganrog, Russia

The paper describes an approach that allows solving the problem of extrinsic calibration of a multi-beam lidar and a stereo camera. The approach does not impose any restrictions on the place in which calibration should be performed. Calibration is performed using a calibration board, which is a flat rectangle with special markers. Three-dimensional correspondences are used for calibration. First, a search for the three-dimensional coordinates of the corner points of the calibration board in the coordinate systems of the stereo pair cameras as well as in the coordinate system of the lidar is made. Next, using the optimization methods, calibration parameters are calculated. The results of a series of virtual and real experiments show that the algorithm allows the calibration to be performed with an accuracy comparable to that of sensors. The proposed approach allows one to improve the calibration accuracy due to the simultaneous use of information from two cameras of the stereo pair and is suitable for lidars with both the low and high point density.

stereo camera, lidar, extrinsic calibration, data fusion.

Abramenko AA. Extrinsic calibration of stereo camera and three-dimensional laser scanner. Computer Optics 2019; 43(2): 220-230. DOI: 10.18287/2412-6179-2019-43-2-220-230.


  1. Goshin YV, Fursov VA. 3D scene reconstruction from stereo images with unknown extrinsic parameters [In Russian]. Computer Optics 2015; 39(5): 770-776. DOI: 10.18287/0134-2452-2015-39-5-770-776.
  2. Minaev EY, Nikonorov AV. High accuracy pose reconstruction of passive colored marker [In Russian]. Computer Optics 2012; 36(4): 611-616.
  3. Zhang Q, Pless R. Extrinsic calibration of a camera and laser range finder (improves camera calibration). 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2004; 3: 2301-2306. DOI: 10.1109/IROS.2004.1389752.
  4. Unnikrishnan R, Martial H. Fast extrinsic calibration of a laser rangefinder to a camera. Tech Rep CMU-RI-TR-05-09. Pittsburgh, PA: Robotics Institute, 2005.
  5. Pandey G, McBride J, Savarese S, Eustice R. Extrinsic calibration of a 3D laser scanner and an omnidirectional camera. IFAC Proceedings Volumes 2010; 43(16): 336-341. DOI: 10.3182/20100906-3-IT-2019.00059.
  6. Huang L., Barth M. A novel multi-planar LIDAR and computer vision calibration procedure using 2D patterns for automated navigation. 2009 IEEE Intelligent Vehicles Symposium 2009: 117-122. DOI: 10.1109/IVS.2009.5164263.
  7. Geiger A, Moosmann F, Car Ö, Schuster B. Automatic camera and range sensor calibration using a single shot. 2012 IEEE International Conference on Robotics and Automation 2012: 3936-3943. DOI: 10.1109/ICRA.2012.6224570.
  8. Pusztai Z, Hajder L. Accurate calibration of LiDAR-camera systems using ordinary boxes. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017: 394-402. DOI: 10.1109/ICCVW.2017.53.
  9. Gong X, Lin Y, Liu J. Extrinsic calibration of a 3D LIDAR and a camera using a trihedron. Optics and Lasers in Engineering. 2013; 51(4): 394-401. DOI: 10.1016/j.optlaseng.2012.11.015.
  10. Fremont V, Rodriguez FSA, Bonnifait P. Circular targets for 3d alignment of video and lidar sensors. Advanced Robotics 2012; 26(18): 2087-2113. DOI: 10.1080/01691864.2012.703235.
  11. Park Y, Yun S, Won CS, Cho K, Um K, Sim S. Calibration between color camera and 3D LIDAR instruments with a polygonal planar board. J Sensors 2014; 14(3): 5333-5353. DOI: 10.3390/s140305333.
  12. Dhall A., Chelani K., Radhakrishnan V., Krishna K.M. LiDAR-Camera Calibration using 3D-3D Point correspondences. arXiv preprint arXiv:1705.09785 2017. Source: < https://arxiv.org/pdf/1705.09785.pdf >.
  13. Veľas M, Španěl M, Materna Z, Herout A. Calibration of rgb camera with velodyne lidar. 2014. Source: < https://pdfs.semanticscholar.org/ed15/5d1a146e0cba6be98fd7128461439f88732a.pdf >.
  14. Levinson J, Thrun S. Automatic online calibration of cameras and lasers. Proceedings of Robotics: Science and Systems 2013. DOI: 10.15607/RSS.2013.IX.029.
  15. Taylor Z, Nieto J. Automatic calibration of lidar and camera images using normalized mutual information. 2013 IEEE International Conference on Robotics and Automation (ICRA) 2013. DOI: 10.1109/ICVES.2015.7396923.
  16. Taylor Z, Nieto J, Johnson D. Automatic calibration of multi-modal sensor systems using a gradient orientation measure. IEEE/RSJ International Conference on Intelligent Robots and Systems 2013: 1293-1300. DOI: 10.1109/IROS.2013.6696516.
  17. Pandey G, McBride JR, Savarese S, Eustice RM. Automatic extrinsic calibration of vision and lidar by maximizing mutual information. Journal of Field Robotics. 2015; 32(5): 696-722. DOI: 10.1002/rob.21542.
  18. John V, Long Q, Liu Z, Mita S. Automatic calibration and registration of lidar and stereo camera without calibration objects. 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2015: 231-237. DOI: 10.1109/ICVES.2015.7396923.
  19. Garrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas FJ, Marín-Jiménez MJ. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition 2014; 47(6): 2280-2292. DOI: 10.1016/j.patcog.2014.01.005.
  20. Kudinov IA, Pavlov OV, Kholopov IS. Implementation of an algorithm for determining the spatial coordinates and the angular orientation of an object based on reference marks, using information from a single camera [In Russian]. Computer Optics 2015; 39(3): 413-419. DOI: 10.18287/0134-2452-2015-39-3-413-419.
  21. Goshin YV, Fursov VA. Solving a camera autocalibration problem with a conformed identification method [In Russian]. Computer Optics 2012; 36(4): 605-610.
  22. Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(11): 1330-1334. DOI: 10.1109/34.888718.
  23. Lepetit V, Moreno-Noguer F, Fua P. EPnP: An accurate O(n) solution to the PnP problem. International Journal of Computer Vision 2009; 81(2): 155. DOI: 10.1007/s11263-008-0152-6.
  24. Gao F, Han L. Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Computational Optimization and Applications 2012; 51(1): 259-277. DOI: 10.1007/s10589-010-9329-3.
  25. Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 1981; 24(6): 381-395. DOI: 10.1145/358669.358692.
  26. Kabsch W. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallographica 1978; 34(5): 827-828. DOI: 10.1107/S0567739478001680.
  27. Sorkine-Hornung O, Rabinovich M. Least-squares rigid motion using svd. 2017. Sourse: < https://igl.ethz.ch/projects/ARAP/svd_rot.pdf >.
  28. Rohmer E, Singh SPN, Freese M. V-REP: A versatile and scalable robot simulation framework. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems 2013: 1321-1326. DOI: 10.1109/IROS.2013.6696520.
  29. Bradski G, Adrian K. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc; 2008.
  30. Hirschmuller Н. Stereo processing by semiglobal matching and mutual information // IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30(2): 328-341. DOI: 10.1109/TPAMI.2007.1166.

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