Calibration of a 3D laser rangefinder and a camera based on optimization solution

Abstract

The calibration of a 3D laser rangefinder (LRF) and a camera is a key technique in the field of computer vision and intelligent robots. This paper proposes a new method for the calibration of a 3D LRF and a camera based on optimization solution. The calibration is achieved by freely moving a checkerboard pattern in front of the camera and the 3D LRF. The images and the 3D point clouds of the checkerboard pattern in various poses are collected by the camera and the 3D LRF respectively. By using the images, the intrinsic parameters and the poses of the checkerboard pattern are obtained. Then, two kinds of geometric constraints, line-to-plane constraints and plane-to-plane constraints, are constructed to solve the extrinsic parameters by linear optimization. Finally, the intrinsic and extrinsic parameters are further refined by global optimization, and are used to compute the geometric mapping relationship between the 3D LRF and the camera. The proposed calibration method is evaluated with both synthetic data and real data. The experimental results show that the proposed calibration method is accurate and robust to noise.

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

“…Simultaneously, through intrinsic calibration, we determine the positional relationship between the calibration board coordinate system and the camera coordinate system for various poses; (3) The extrinsic calibration of the 3D LiDAR and camera involves determining the geometric mapping relationship between the 3D point cloud and image pixels. Our research group has developed a method for calibrating the extrinsic parameters [25]. This method incorporates both line-plane and plane-plane geometric constraints to create a set of linear equations.…”

Stereo visual odometry based on dynamic and static features division

“…Specifically, the map points at k − 1 and k are obtained by Equations ( 1), (2). Motion changes between two moments are obtained as follows:…”

“…In general, the small proportion of moving objects is viewed as a type of noise. To overcome this noise, an optimization method that uses laser data to calibrate the camera was proposed in [2]. However, the use of new sensors is costly and needs to deal with the convex constraints of the nonlinear equation [11].…”

“…( 1) Combining the SPDIP and the scene flow allows us to distinguish accurately between the dynamic and static features. (2) We propose a new threshold for the division of motion points. (3) We apply a new similarity calculation function for LCD.…”