This paper proposes an automated method to obtain the extrinsic calibration parameters between a camera and a 3D lidar with as low as 16 beams. We use a checkerboard as a reference to obtain features of interest in both sensor frames. The calibration board centre point and normal vector are automatically extracted from the lidar point cloud by exploiting the geometry of the board. The corresponding features in the camera image are obtained from the camera's extrinsic matrix. We explain the reasons behind selecting these features, and why they are more robust compared to other possibilities. To obtain the optimal extrinsic parameters, we choose a genetic algorithm to address the highly non-linear state space. The process is automated after defining the bounds of the 3D experimental region relative to the lidar, and the true board dimensions. In addition, the camera is assumed to be intrinsically calibrated. Our method requires a minimum of 3 checkerboard poses, and the calibration accuracy is demonstrated by evaluating our algorithm using real world and simulated features.
@cite_19: In this paper, we address the problem of extrinsic calibration of a camera and a 3D Light Detection and Ranging (LiDAR) sensor using a checkerboard. Unlike previous works which require at least three checkerboard poses, our algorithm reduces the minimal number of poses to one by combining 3D line and plane correspondences. Besides, we prove that parallel planar targets with parallel boundaries provide the same constraints in our algorithm. This allows us to place the checkerboard close to the LiDAR so that the laser points better approximate the target boundary without loss of generality. Moreover, we present an algorithm to estimate the similarity transformation between the LiDAR and the camera for the applications where only the correspondences between laser points and pixels are concerned. Using a similarity transformation can simplify the calibration process since the physical size of the checkerboard is not needed. Meanwhile, estimating the scale can yield a more accurate result due to the inevitable measurement errors of the checkerboard size and the LiDAR intrinsic scale factor that transforms the LiDAR measurement to the metric measurement. Our algorithm is validated through simulations and experiments. Compared to the plane-only algorithms, our algorithm can obtain more accurate result by fewer number of poses. This is beneficial to the large-scale commercial application.
More recently in @cite_19 , points corresponding to the edges of the checkerboard were used to fit lines. The corresponding edge lines were obtained from the camera and constraints were formed using plane-line correspondence. As explained in the previous section, these features are more prone to errors and hence we avoid using them.