Extrinsic multi-sensor calibration for mobile robots using the Gauss-Helmert model

Most state estimation procedures in mobile robotics require information about the locations of the individual sensors on the platform. In this paper, we study the motion-based multi-sensor extrinsic calibration problem and point out an overlooked defect of traditional least squares estimation in this context. We present a novel calibration approach based-on the Gauss-Helmert estimation paradigm, together with a formulation for multi-sensor motion constraint. Our approach estimates not only the extrinsic parameters but also the pose observation errors, thus recovering the underlying sensor movements that exactly fulfill the motion constraints. Compared to traditional least squares approaches that estimate only the parameters, our approach is statistically optimal, thus is more accurate and robust. We implemented our approach and tested it on real robot. The experiments show that our approach is able to accurately determine the extrinsic configuration of each sensor and can largely improve the accuracy when the noise level is high.