Efficient Ego-Motion Estimation for Multi-Camera Systems With Decoupled Rotation and Translation

Abstract

In this article, we present novel solutions to estimate the ego-motion of a multi-camera system with a known vertical direction (e.g., from the inertial measurement unit). By assuming small camera motion between successive video frames, we demonstrate that rotation and translation estimation can be decoupled. This makes our methods require fewer correspondences to estimate the ego-motion and have a good accuracy. Accordingly, we estimate the ego-motion with two steps. First, we propose a 1-point method to estimate rotation with only a single correspondence which produces up to two solutions. Then, we adopt a 3-point linear method and a 2-point sampling method to solve translation which produce a single solution. We compared our algorithms with state-of-the-art algorithms on synthetic and real datasets. The experiments demonstrate that our algorithms are accurate and efficient in road driving scenarios. We also demonstrate that our proposed methods can efficiently find an optimal inlier set using histogram voting or exhaustive search instead of RANSAC.