可扩展的户外自主无人机飞行：基于视觉-惯性SLAM和无LiDAR的密集子地图构建
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
AI总结：
本文提出了一种基于视觉-惯性SLAM和无LiDAR的自主无人机系统，实现户外复杂环境下的大规模自主导航和路径规划。
摘要：
自主导航对于多种机器人应用至关重要。本文提出了一种自主微型飞行器（MAV）系统，该系统仅依赖于成本低廉且轻量的被动视觉和惯性传感器，在户外、非结构化和杂乱的环境中实现大范围自主导航。我们利用视觉-惯性同时定位与建图（VI-SLAM）来获得精确的MAV状态估计，并结合体积占用子地图系统，实现可扩展的建图框架，可以直接用于路径规划。为确保MAV在导航过程中的安全性，我们还提出了一种新的参考轨迹锚定方案，该方案在VI-SLAM系统状态更新时以一致的方式变形MAV所跟踪的参考轨迹，即使在由于回环闭合导致的大状态更新时也是如此。我们彻底验证了该系统在真实和模拟森林环境中的性能，并在峰值速度高达3 m/s时未遭遇任何碰撞或系统故障。据我们所知，这是首次在如此非结构化环境中使用低成本被动视觉传感器和完全 onboard 计算实现此类性能的系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
