Abstract
Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.
Highlights
A considerable amount of literature has proliferated around the theme of multi-sensor systems in 3D object detection and recognition [1], simultaneous localization and mapping (SLAM) [2], path planning [3], reconstruction scenes [4,5], and other fields
We present a novel method to calibrate the intrinsic parameters of the camera and extrinsic parameters of the system
The calibration calibration target target is in aa suitable suitable position, position, tion, is in where it can be captured by the camera and
Summary
A considerable amount of literature has proliferated around the theme of multi-sensor systems in 3D object detection and recognition [1], simultaneous localization and mapping (SLAM) [2], path planning [3], reconstruction scenes [4,5], and other fields. LiDAR is first developed and used in many applications: urban planning, urban mapping, intelligent autonomous transport, and scanning forests and agricultural fields [7,8,9] It is a mature technology, the use of a mechanical scanner increases the system size and complexity, while challenging the reliability of the moving parts under long-term use. A solid-state LiDAR is usually a radar with no moving parts at all It mainly includes micro electro mechanical systems (MEMS) LiDAR, optical phased array (OPA) LiDAR, and flash. We present a novel method to calibrate the intrinsic parameters of the camera and extrinsic parameters of the system. Our method only employs planes of triangular pyramid from camera images and LiDAR point clouds in calibration.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
