An Efficient Method to Recover Relative Pose for Vehicle-Mounted Cameras Under Planar Motion

In this paper, a 2-point algorithm is proposed to estimate the relative pose as well as the absolute scale between two vehicle-mounted cameras efficiently. The system model is deduced by combining a two-view geometric model and the planar motion constraint to reduce the degrees of freedom. 2-point correspondences are utilized to calculate the rotation information independently from the translation information, which indicates that the proposed algorithm can deal with pure rotation scenes. Besides, provided that the camera’s configuration satisfies a certain condition, the absolute scale can be recovered. An approximation algorithm is developed and combined with the random sample and consensus scheme to deal with the uneven ground surfaces in practice. As only 2-point correspondences are required, less iterations are demanded in the estimating procedure compared with many other existing related algorithms. Both simulation and experiments are implemented to evaluate the proposed algorithm, in which the synthetic data, virtual robot experimentation platform, KITTI Vision Benchmark, and SUMMIT-XL platform are acquired. According to the results, the proposed algorithm performs better than many related algorithms including the well-known 5-point algorithm in many cases, especially when the camera’s trajectory contains sharp corners.