Abstract
This paper proposes a simple extrinsic calibration method for a multi-sensor system which consists of six image cameras and a 16-channel 3D LiDAR sensor using a planar chessboard. The six cameras are mounted on a specially designed hexagonal plate to capture omnidirectional images and the LiDAR sensor is mounted on the top of the plates to capture 3D points in 360 degrees. Considering each camera–LiDAR combination as an independent multi-sensor unit, the rotation and translation between the two sensor coordinates are calibrated. The 2D chessboard corners in the camera image are reprojected into 3D space to fit to a 3D plane with respect to the camera coordinate system. The corresponding 3D point data that scan the chessboard are used to fit to another 3D plane with respect to the LiDAR coordinate system. The rotation matrix is calculated by aligning normal vectors of the corresponding planes. In addition, an arbitrary point on the 3D camera plane is projected to a 3D point on the LiDAR plane, and the distance between the two points are iteratively minimized to estimate the translation matrix. At least three or more planes are used to find accurate external parameters between the coordinate systems. Finally, the estimated transformation is refined using the distance between all chessboard 3D points and the LiDAR plane. In the experiments, quantitative error analysis is done using a simulation tool and real test sequences are also used for calibration consistency analysis.
Highlights
Three-dimensional mapping and localization techniques for autonomous vehicles [1,2,3] have been investigated extensively for over the years
Accurate extrinsic calibration between cameras and LiDAR sensors increase the reliability of data fusions between the color and shape information of 3D point data
We propose that findsbetween the external parameters between a single we propose a calibration algorithm that finds the external parameters between a single camera and camera and the LiDAR
Summary
Three-dimensional mapping and localization techniques for autonomous vehicles [1,2,3] have been investigated extensively for over the years. Accurate extrinsic calibration between cameras and LiDAR sensors increase the reliability of data fusions between the color and shape information of 3D point data. Many extrinsic calibration methods between 360-degree LiDAR and vision camera have been proposed. Using the 2D and 3D correspondences of the center coordinates, the extrinsic parameters are estimated using the PnP algorithm Their method is simple and provides stable performance. The rotation and translation between a LiDAR and camera are estimated by minimizing an energy function of correspondences between depth and image frames. Extrinsic parameters between a camera and LiDAR are estimated using these correspondences obtained from a single pose of the chessboard. Huang and Barth [12] propose an extrinsic calibration method using geometric constraints of the views of chessboard from the LiDAR data and camera image.
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