Online extrinsic multi-camera calibration using ground plane induced homographies

Abstract

This paper presents an approach for online estimation of the extrinsic calibration parameters of a multicamera rig. Given a coarse initial estimate of the parameters, the relative poses between cameras are refined through recursive filtering. The approach is purely vision based and relies on plane induced homographies between successive frames. Overlapping fields of view are not required. Instead, the ground plane serves as a natural reference object. In contrast to other approaches, motion, relative camera poses, and the ground plane are estimated simultaneously using a single iterated extended Kalman filter. This reduces not only the number of parameters but also the computational complexity. Furthermore, an arbitrary number of cameras can be incorporated. Several experiments on synthetic as well as real data were conducted using a setup of four synchronized wide angle fisheye cameras, mounted on a moving platform. Results were obtained, using both, a planar and a general motion model with full six degrees of freedom. Additionally, the effects of uncertain intrinsic parameters and nonplanar ground were evaluated experimentally.

Global calibration of non-overlapping cameras: State of the art

“…The experimental results show that the system achieves 0.1 mm measurement accuracy in the range of 1353 mm. Other works based on combined techniques are described in [91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106] to implement global calibration. Fig.…”

Calibration of Non-overlapping Cameras Using an External SLAM System

“…In contrast, we leverage an external SLAM system independent of the cameras to be calibrated; thus our method is applicable to stationary cameras. Knorr et al [18] also assumed the motion of cameras and presented an approach for refining the extrinsic parameters during online operations by using the homographies computed with respect to the ground plane in an extended Kalman filter framework.…”

Global homography calibration for monocular vision-based pose measurement of mobile robots

“…To measure the relative position and orientation between an object frame and a camera frame, monocular vision (Wu et al 2015) and stereo vision (Yang et al 2016) are usually employed as effective measurement tools. In general, monocular visual measurement uses the inherent geometric relationship among the target points (Wolfe et al 1991), while binocular stereo visual measurement uses triangulation to obtain the coordinates of the object in the camera frame (Zhai et al 2015;Knorr et al 2013). This paper will focus on visual measurement with monocular vision.…”