Abstract—This paper describes a novel method for deter- mining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

  • [PDF] W. Maddern, A. Harrison, and P. Newman, “Lost in Translation (and Rotation): Fast Extrinsic Calibration for 2D and 3D LIDARs,” in Proc. IEEE International Conference on Robotics and Automation (ICRA), Minnesota, USA, 2012.
    [Bibtex]

    @inproceedings{MaddernICRA2012,
    Address = {Minnesota, USA},
    Author = {Will Maddern and Alastair Harrison and Paul Newman},
    Booktitle = {Proc. IEEE International Conference on Robotics and Automation (ICRA)},
    Date-Modified = {2012-03-06 10:12:42 +0000},
    Keywords = {2D and 3D Laser Extrinsic Calibration},
    Month = {May},
    Owner = {ashley},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2012ICRA_wm_arh_pmn.pdf},
    Timestamp = {2012.02.02},
    Title = {Lost in Translation (and Rotation): Fast Extrinsic Calibration for 2D and 3D LIDARs},
    Year = {2012}}