Visual SLAM With Drift-Free Rotation Estimation in Manhattan World

Abstract

This letter presents an efficient and accurate simultaneous localization and mapping (SLAM) system in man-made environments. The Manhattan world assumption is imposed, with which the global orientation is obtained. The drift-free rotational motion estimation is derived from the structural regularities using line features. In particular, a two-stage vanishing points (VPs) estimation method is developed, which consists of a short-term tracking module to track the clustered line features and a long-term searching module to generate abundant sets of VPs candidates and retrieve the optimal one. A least square problem is constructed and solved to provide refined VPs with the clusters of structural line features every frame. We make full use of the absolute orientation estimation to benefit the whole SLAM process. In particular, we utilize the absolute orientation estimation to increase the localization accuracy in the front end, and formulate a linear batch camera pose refinement problem with the known rotations to improve the real time performance in the back end. Experiments on both synthesized and real-world scenes reveal results with high-precision in the real time camera pose estimation process and high-speed in pose graph optimization process compared with the existing state-of-the-art methods.