Visual laser-SLAM in large-scale indoor environments

Abstract

Loop closure is a well-known problem in the research of laser based simultaneous localization and mapping, especially for applications in large-scale environments. The cumulative errors in the estimated pose and map make the loop detection difficult, no matter using particle filter-based or graph-based SLAM methods. Camera has the advantage of rich information but suffers from short distance and relative high computation burden. In this paper, we proposed a novel approach to address the loop closure problem in large-scale laser-SLAMs, where both laser and camera sensors are integrated. ORB features and bags-of-word were applied to obtain fast and robust performance of loop detection. The well-recognized LRGC SLAM framework and SPA optimization algorithm were then used to achieve the SLAM. Finally, several experiments in different large-scale environments were performed to verify the effectiveness of the proposed approach.