LiDAR不要のVisual-Inertial SLAMと高密度サブマップによるスケーラブルな屋外自律ドローン飛行
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
低コストな視覚・慣性センサのみを用いたVI-SLAMと高密度占有サブマップを組み合わせ、森林のような非構造屋外環境で最大3m/sの自律飛行を衝突なしに実現したシステムを提案。
著者: Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou, Dimos Tzoumanikas, Simon Schaefer, Hanzhi Chen, Stefan Leutenegger
分類: cs.RO
原文アブストラクト
Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform large-scale autonomous navigation in outdoor,unstructured and cluttered environments. We leverage visual-inertial simultaneous localization and mapping (VI-SLAM) for accurate MAV state estimates and couple it with a volumetric occupancy submapping system to achieve a scalable mapping framework which can be directly used for path planning. To ensure the safety of the MAV during navigation, we also propose a novel reference trajectory anchoring scheme that deforms the reference trajectory the MAV is tracking upon state updates from the VI-SLAM system in a consistent way, even upon large state updates due to loop-closures. We thoroughly validate our system in both real and simulated forest environments and at peak velocities up to 3 m/s while not encountering a single collision or system failure. To the best of our knowledge, this is the first system which achieves this level of performance in such an unstructured environment using low-cost passive visual sensors and fully on-board computation, including VI-SLAM.
