  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.
