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This paper proposes an automated method to obtain the extrinsic calibrationparameters between a camera and a 3D lidar with as low as 16 beams. We use acheckerboard as a reference to obtain features of interest in both sensorframes. The calibration board centre point and normal vector are automaticallyextracted from the lidar point cloud by exploiting the geometry of the board.The corresponding features in the camera image are obtained from the camera'sextrinsic matrix. We explain the reasons behind selecting these features, andwhy they are more robust compared to other possibilities. To obtain the optimalextrinsic parameters, we choose a genetic algorithm to address the highlynon-linear state space. The process is automated after defining the bounds ofthe 3D experimental region relative to the lidar, and the true boarddimensions. In addition, the camera is assumed to be intrinsically calibrated.Our method requires a minimum of 3 checkerboard poses, and the calibrationaccuracy is demonstrated by evaluating our algorithm using real world andsimulated features.

Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences