A Novel Robust Approach for CorrespondenceFree Extrinsic Calibration 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 correspondencefree 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.

Extrinsic Calibration Between a Stereo System and a 3D LIDAR As the robot gradually develops from indoor to outdoor, multisensor fusion is increasingly being applied to the field of robot perception. Among them, LiDAR and binocular sensors are most widely used. Robots use the complementary and redundant data they provide to perform environmental perception and implement certain functions of the robot, such as SLAM, autonomous obstacle avoidance and so on. However, the data obtained by each sensor are relative to its own coordinate system. Data must be converted to a unified coordinate system before data fusion. Therefore, the calibration of the transformation among sensors is the first problem to be solved in data fusion. Moreover, the accuracy of calibration is directly related to the effect of data fusion. Therefore the calibration between sensors is very important and necessary. In this paper, we propose a novel method to find accurate rigidbody transformation for the extrinsic calibration of a 3D LiDAR and a stereo cameras, using two Aruco calibration boards. Also the validity of the algorithm is demonstrated by some experiments.