Novel robust simultaneous localization and mapping for long-term autonomous robots

Abstract

A fundamental task for mobile robots is simultaneous localization and mapping (SLAM). Moreover, long-term robustness is an important property for SLAM. When vehicles or robots steer fast or steer in certain scenarios, such as low-texture environments, long corridors, tunnels, or other duplicated structural environments, most SLAM systems might fail. In this paper, we propose a novel robust visual inertial light detection and ranging (LiDaR) navigation (VILN) SLAM system, including stereo visual-inertial LiDaR odometry and visual-LiDaR loop closure. The proposed VILN SLAM system can perform well with low drift after long-term experiments, even when the LiDaR or visual measurements are degraded occasionally in complex scenes. Extensive experimental results show that the robustness has been greatly improved in various scenarios compared to state-of-the-art SLAM systems.