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...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(Li Da R)navigation(VILN)SLAM system,including stereo visual-inertial Li Da R odometry and visual-Li Da R loop closure.The proposed VILN SLAM system can perform well with low drift after long-term experiments,even when the Li Da R 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.展开更多
基金Project supported by the National Key R&D Program of China(No.2018YFB1305500)the National Natural Science Foundation of China(No.U1813219)。
文摘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(Li Da R)navigation(VILN)SLAM system,including stereo visual-inertial Li Da R odometry and visual-Li Da R loop closure.The proposed VILN SLAM system can perform well with low drift after long-term experiments,even when the Li Da R 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.
