• jihun park hongik university
Keywords: 3D reconstruction; LiDAR camera calibration; coordinate transformation


In this paper, we propose a method to reduce errors in the method of finding the relationship between the LiDAR and the camera using trihedron. The core concept of the proposed technique is to use a trihedron with three intersecting planes that can be commonly recognized by the camera and LiDAR, eliminating the calculation of the coordinate system photographed using the camera. The coordinate system setting operation is performed only for the LiDAR sense data. By using the method of this paper, it is possible to reduce the calculation of the coordinate system using data fitting by camera, so that the error decreases. The result is presented through the experimental results.


Download data is not yet available.


Chen, X. Z., Kundu, K., Zhu, Y., Fidle, S., Urtasun, R. and Ma, H. 2018. 3D object proposals using stereo imagery for accurate object class detection, IEEE Trans. Pattern Anal. Machine Intelligence, vol. 40, no. 5, pp. 1259-1272.

Chavez-Garcia , R. O. and Aycard, O. 2016. Multiple sensor fusion and classification for moving object detection and tracking, IEEE Trans. Intelligent Transportation System, vol. 17, no. 2, pp. 525-534.

Unnikrishnan R. and Hebert, M. 2005. Fast extrinsic calibration of a laser rangefinder to a camera, Robotics Institute, Pittsburgh, PA, USA, Tech. Rep. CMU-RI-TR-05-09.

Naroditsky, O., Patterson, A. and Daniilidis, K. 2011. Automatic align-ment of a camera with a line scan LiDAR system, in Proc. IEEE Int. Conf. Robot. Automat., pp. 3429-3434.

Geiger, A., Moosmann, F., Car, O. and Schuster, B. 2012.Automatic camera and range sensor calibration using a single shot, in Proc. IEEE Int. Conf. Robot. Automat., pp. 3936-3943.

Bileschi, S. 2009. Fully automatic calibration of LiDAR and video streams from a vehicle, in Proc. IEEE 12th Int. Conf. Computer Vision Workshops (ICCV), pp. 1457-1464.

Pandey, G., McBride, J. R., Savarese, S. and Eustice, R. M. 2015. Automatic extrinsic calibration of vision and LiDAR by maximizing mutual information, J. Field Robot., vol. 32, no. 5, pp. 696-722.

Hartley, R. and Zisserman, 2004. A. Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd edition.

Pollefeys, M., Koch, R. and Gool, L. V. 1999. Self- calibration and metric reconstruction in spite of varying and unknown intrinsic camera parameters, International Journal of Computer Vision, Vol. 32, No. 1, pp. 7-25.

Moons, T., van Gool, L., and Vergauwen, M. 2008. Foundation and Trends in Computer Graphics and Vision 4(4), 287–404.

Zhang, Z. 2000. A flexible new technique for camera calibration, IEEE Trans. Pattern Analysis and Machine Intelligence 22(11), 1330–1334 November.

Lowe, D. G. 2004. Distinctive image features from scale-invariant key points, International Journal of Computer Vision 60(2), 91–110 Nov.

Park, J. and Park, S. 2013. Improvement on Zhang's camera calibration, Applied Mechanics and Materials 479-480, 170–173.

Gong, X., Lin, Y. and Liu, J. 2013. 3D LiDAR -Camera Extrinsic Calibration Using an Arbitrary Trihedron; Sensors 13(2):1902-18.

Park, J. 2020. LiDAR Camera Calibration Using 3D Reconstruction of a Trihedron (in Korean), Journal of Next-generation Convergence Technology Association, Vol.4, No.6, pp. 623-629.

How to Cite
park, jihun. (2021). REDUCING ERROR IN LIDAR-CAMERA CALIBRATION. COMPUSOFT: An International Journal of Advanced Computer Technology, 10(5), 3973-3977. Retrieved from