3D LiDAR Extrinsic Calibration Method using Ground Plane Model Estimation In the context of road defects detection, especially potholes detection, using a 3D LiDAR mounted on a vehicle seems to be an interesting way to provide location and geometric information about the defects. In this paper, an extrinsic calibration method is proposed, to merge all the LiDAR frames in a common reference frame and to define the positioning of the sensor. This method depends mainly on the geometrical ground impact model in order to estimate the LiDAR extrinsic parameters by successive steps algorithm: fitting plane, Euleru0027s angles estimation of rotation, height estimation and parameters optimization. Results are presented in terms of precision and robustness against the LiDAR range accuracy proving the performance of this calibration method.

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.