However, while marking, if there seem to be missing/less points on the cardboard edges, tweaking this value will might help. The default value at which it works well is 0.05. The cloud_intensity_threshold is used to filter points that have intensity lower than a specified value. The filtered pointcloud contains all points The filtred point cloud makes it easier to mark the board edges. X- and x+, y- and y+, z- and z+ are used to remove unwanted points in the cloud and are specfied in meters. Initial_rot_x initial_rot_y initial_rot_z The file contains specifications about the following:
The find_transform.launch file is available in the lidar_camera_calibration/launch directory. The config files are available in the lidar_camera_calibration/conf directory. There are a couple of configuration files that need to be specfied in order to calibrate the camera and the LiDAR. Hence the two separate catkin_make commands.Ĭamera parameters will also be required by the package, so it is advised that you calibrate the camera beforehand. Installing them together, in one run, can result in build errors. The aruco packages have to be installed before the main calibration package can be installed. Please note that aruco_mapping in the dependencies folder is a slightly modified version of the original aruco_mapping, so make sure to use the one provided here. Please cite our work if lidar_camera_calibration and our approach helps your research.Ĭatkin_make -DCATKIN_WHITELIST_PACKAGES = "aruco aruco_ros aruco_msgs"Ĭatkin_make -DCATKIN_WHITELIST_PACKAGES = "aruco_mapping lidar_camera_calibration"Ĭatkin_make -DCATKIN_WHITELIST_PACKAGES = "" Both of which were extrinsically calibrated using a LiDAR and lidar_camera_calibration.įor more details please refer to our arXiv paper. The lidar_camera_calibration/pointcloud_fusion provides a script to fuse point clouds obtained from two stereo cameras. The package uses aruco_ros and a slightly modified aruco_mapping as dependencies, both of which are available in the dependencies folder in this repository. The package finds a rotation and translation that transform all the points in the LiDAR frame to the (monocular) camera frame. See Fusion using lidar_camera_calibration for results of the point cloud fusion (videos). We show the accuracy of the proposed pipeline by fusing point clouds, with near perfection, from multiple cameras kept in various positions.
Since, VLP-16 provides only 16 rings, we believe that the higher models of the Velodyne will also work well with this package. Specficially, Point Gray Blackfly and ZED camera have been successfully calibrated against Velodyne VLP-16 using lidar_camera_calibration.
The package is used to calibrate a Velodyne LiDAR with a camera (works for both monocular and stereo). ROS package to calibrate a camera and a LiDAR. LiDAR-Camera Calibration using 3D-3D Point correspondencesĪnkit Dhall, Kunal Chelani, Vishnu Radhakrishnan, KM Krishna
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