4/12/2021 0 Comments Camera Calibration Toolbox Matlab
References 1 Lipu Zhou and Zimo Li and Michael Kaess, Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences, IEEERSJ Intl. Conf. on Intelligent Robots and Systems, IROS, Oct, 2018. 2 K. S. Arun, T. S. Huang, and S. D. Blostein, Least-squares fitting of two 3-D point sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 5, pp. 698700, Sept 1987.At the end of this example, you will be able to use the rigid transformation matrix to fuse lidar and camera data.This diagram explains the workflow for the lidar and camera calibration (LCC) process.
Overview Lidar and camera sensors are the most common vision sensors in autonomous driving applications. Cameras provide rich color information and other features that can be used to extract different characteristics of the detected objects. Lidar sensors, on the other hand, provide an accurate 3-D location and structure of the objects. To enhance the object detection and classification pipeline, data from these two sensors can be fused together to get more detailed and accurate information on the objects. The transformation matrix in the form of orientation and relative positions between the two sensors is the precursor to fusing data from these two sensors. Lidar camera calibration helps in estimating the transformation matrix between 3-D lidar and a camera mounted on the autonomous vehicle. In this example, you will use data from two different lidar sensors, HDL64 and VLP16. HDL64 data is collected from a Gazebo environment as shown in this figure. Data is captured in the form of set of PNG images and corresponding PCD point clouds. This example assumes that the cameras intrinsic parameters are known. For more information on extracting a cameras intrinsic parameters, see Single Camera Calibration. Checkerboard edges are estimated using lidar and camera sensors. Use estimateCheckerboardCorners3d to calculate coordinates of the checkerboard corners and size of actual checkerboard in mm. The corners are estimated in 3-D with respect to the cameras coordinate system. For more details on camera coordinate systems, see Coordinate Systems in Lidar Toolbox imageCorners3d, checkerboardDimension, dataUsed. The function detects rectangular objects in the point cloud based on the input dimensions. Enhance Lidar point cloud using color infromation from image. Use helperFuseLidarCamera function to visualize the lidar and the image data fused together. Rotation Error: Mean of difference between the normals of checkerboard in the point cloud and the projected corners in 3-D from an image. Reprojection Error: Mean of difference between the centroid of image corners and projected lidar corners on the image. Camera Calibration Toolbox Matlab How To Use TheThis example also shows you how to use the rigid transformation matrix to fuse lidar and camera data. References 1 Lipu Zhou and Zimo Li and Michael Kaess, Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences, IEEERSJ Intl. Conf. on Intelligent Robots and Systems, IROS, Oct, 2018. K. S. Arun, T. S. Huang, and S. D. Blostein, Least-squares fitting of two 3-D point sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 5, pp.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |