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.

Automatic Extrinsic Calibration of LIDAR and Monocular Camera Images Mobile robots are being equipped with an ever increasing amount of sensors, and by taking advantage of each modalityxe2x80x99s forte, through sensor fusion we are able to produce superior results for higher level functions such as pedestrian detection. An accurate calibration between the sensors, however, is essential for obtaining a correct fusion. One of the more common setups is that of an optical camera paired with a lidar system, complementing each otherxe2x80x99s data. While the camera offers rich color information through a dense image, the lidar produces high accuracy, though sparse depth measurements. In this paper we will present two methods for estimating the 6 degrees of freedom parameters necessary to extrinsically calibrate the two sensors, without the use of special calibration targets: the first one attempts to align edges detected in both the lidar point cloud and the corresponding color image, while the second approach minimizes the depth difference between the measured lidar data and a depth map generated from the corresponding monocular image. By defining correlations between the two modalityxe2x80x99s information, we are able to construct an optimization problem on the 6 calibration parameters. Our experiments show good results when matched against similar solutions proposed in the literature, especially in the case of the calibration algorithm based on depth estimation.