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.
Knorr et al. REF 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.
Online extrinsic multi-camera calibration using ground plane induced homographies
2013 IEEE Intelligent Vehicles Symposium (IV)
2013 IEEE Intelligent Vehicles Symposium (IV)
Mathematics Computer Science