A fast calibration approach for onboard LiDAR-camera systems

Abstract

Outdoor surveillance and security robots have a wide range of industrial, military, and civilian applications. In order to achieve autonomous navigation, the LiDAR-camera system is widely applied by outdoor surveillance and security robots. The calibration of the LiDAR-camera system is essential and important for robots to correctly acquire the scene information. This article proposes a fast calibration approach that is different from traditional calibration algorithms. The proposed approach combines two independent calibration processes, which are the calibration of LiDAR and the camera to robot platform, so as to address the relationship between LiDAR sensor and camera sensor. A novel approach to calibrate LiDAR to robot platform is applied to improve accuracy and robustness. A series of indoor experiments are carried out and the results show that the proposed approach is effective and efficient. At last, it is applied to our own outdoor security robot platform to detect both positive and negative obstacles in a field environment, in which two Velodyne-HDL-32 LiDARs and a color camera are employed. The real application illustrates the robustness performance of the proposed approach.

Camera–LiDAR Calibration Using Iterative Random Sampling and Intersection Line-Based Quality Evaluation

“…This method estimates extrinsic parameters by calculating the rigid transformation between the corresponding sphere centers based on the least squares method. Erke et al [31] utilized three orthogonal planes with a checkerboard printed on one of them. This method finds extrinsic parameters by separately conducting LiDAR-robot and camera-robot calibrations.…”

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

“…The target-based method is the most commonly used approach for LiDAR–camera calibration. This method uses markers such as chessboard patterns to establish data association and performs calibration by minimizing reprojection errors between different sensors [ 2 , 3 , 4 ]. This method often requires specific calibration setups and even manual intervention.…”