Extrinsic calibration is a necessary step when using heterogeneous sensors for robotics applications. Most existing methods work under the assumption that the prior data correspondence is known. Considering data loss and false measurements, the correspondence may not be accessible in practice. To solve this problem without knowing the correspondence, several probabilistic methods have been proposed. However, an implicit restriction on input data limits their application. Therefore, in this paper, we propose a more stable correspondence-free method with two improvements that can relax the restrictions on inputs and improve the calibration accuracy. The first improvement finds consistent sets from raw inputs using screw invariants, which significantly improve the robustness in case of outliers and data loss. A new optimization method on matrix Lie group is proposed as the second improvement, which demonstrates better accuracy. The experimental results on both numerical and real data show the superiority and robustness of the proposed method.
|Title of host publication||2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||International Conference on Intelligent Robots and Systems (IROS 2019) - Macau, China|
Duration: 3 Nov 2019 → 8 Nov 2019
|Conference||International Conference on Intelligent Robots and Systems (IROS 2019)|
|Period||03/11/2019 → 08/11/2019|