A Novel Method for Automatic Extrinsic Parameter Calibration of RGB-D Cameras

Abstract

Calibration of extrinsic parameters of the RGB-D camera can be applied in many fields, such as 3D scene reconstruction, robotics, and target detection. Many calibration methods employ a specific calibration object (i.e., a chessboard, cuboid, etc.) to calibrate the extrinsic parameters of the RGB-D color camera without using the depth map. As a result, it is difficult to simplify the calibration process, and the color sensor gets calibrated instead of the depth sensor. To this end, we propose a method that employs the depth map to perform extrinsic calibration automatically. In detail, the depth map is first transformed to a 3D point cloud in the camera coordinate system, and then the planes in the 3D point cloud are automatically detected using the Maximum Likelihood Estimation Sample Consensus (MLESAC) method. After that, according to the constraint relationship between the ground plane and the world coordinate system, all planes are traversed and screened until the ground plane is obtained. Finally, the extrinsic parameters are calculated using the spatial relationship between the ground plane and the camera coordinate system. The results show that the mean roll angle error of extrinsic parameter calibration was −1.14°. The mean pitch angle error was 4.57°, and the mean camera height error was 3.96 cm. The proposed method can accurately and automatically estimate the extrinsic parameters of a camera. Furthermore, after parallel optimization, it can achieve real-time performance for automatically estimating a robot’s attitude.