TY - JOUR
T1 - 3D LIDAR-camera intrinsic and extrinsic calibration
T2 - Identifiability and analytical least-squares-based initialization
AU - Mirzaei, Faraz M.
AU - Kottas, Dimitrios G.
AU - Roumeliotis, Stergios I.
PY - 2012/4
Y1 - 2012/4
N2 - In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal identifiability conditions, under which it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.
AB - In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal identifiability conditions, under which it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.
KW - Sensing and perception
KW - calibration and identification
KW - computer vision
KW - range sensing
UR - http://www.scopus.com/inward/record.url?scp=84859567352&partnerID=8YFLogxK
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U2 - 10.1177/0278364911435689
DO - 10.1177/0278364911435689
M3 - Article
AN - SCOPUS:84859567352
SN - 0278-3649
VL - 31
SP - 452
EP - 467
JO - International Journal of Robotics Research
JF - International Journal of Robotics Research
IS - 4
ER -