A calibration method with anistropic weighting for LiDAR and stereo camera system Calibrating the extrinsic matrices between sensors is a significant preprocessing step of sensor fusion. Most of existing calibration methods use pointbased rigid registration algorithm which considers the point coordinate error isotropic and uses the least square solution to estimate the extrinsic matrices. However, the error distribution of point coordinates is anisotropic due to the internal measurement properties of sensors, leading to decreased calibration accuracy. To solve this problem, we proposed an anisotropy weighting method: first we construct weighting matrices based on error distributions models of sensors; second we use surveying adjustment to further improve the calibration accuracy iteratively. We verified the effectiveness of our method through simulations. Compared with traditional methods, the accuracy is improved by about 45%. Moreover, our method can be applied in most of calibration methods to reduce the influence of anisotropic data and improve the accuracy.

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.