Forestry constitutes a key element for a sustainable future, while it is supremely challenging to introduce digital processes to improve efficiency. The main limitation is the difficulty of obtaining accurate maps at high temporal and spatial resolution as a basis for informed forestry decision-making, due to the vast area forests extend over and the sheer number of trees. To address this challenge, 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 under-canopy autonomous navigation. 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. As opposed to a monolithic map, submaps inherently deal with inevitable drift and corrections from VI-SLAM, since they move with pose estimates as they are updated. To ensure the safety of the MAV during navigation, we also propose a novel reference trajectory anchoring scheme that moves and deforms the reference trajectory the MAV is tracking upon state updates from the VI-SLAM system in a consistent way, even upon large changes in state estimates due to loop-closures. We thoroughly validate our system in both real and simulated forest environments with high tree densities in excess of 400 trees per hectare and at speeds 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 unstructured environment using low-cost passive visual sensors and fully on-board computation including VI-SLAM.

翻译：森林对于可持续未来至关重要，但引入数字化流程以提高效率极为困难。主要限制在于，由于森林覆盖面积广阔且树木数量庞大，难以获取高时间与空间分辨率的精确地图作为明智林业决策的基础。为应对这一挑战，我们提出了一种自主微型飞行器（MAV）系统，该系统仅依赖低成本、轻量化的被动视觉与惯性传感器，实现林冠下的自主导航。我们利用视觉-惯性同步定位与建图（VI-SLAM）实现精确的MAV状态估计，并将其与体积占据子地图建图系统相结合，构建可直接用于路径规划的可扩展建图框架。与全局建图不同，子地图天然能够处理VI-SLAM中不可避免的漂移与修正，因为它们会随更新时的位姿估计移动。为确保MAV在导航过程中的安全性，我们还提出了一种新颖的参考轨迹锚定方案，该方案能在VI-SLAM系统状态更新时（包括闭环修正导致的大幅状态变化）以一致的方式移动和变形MAV所跟踪的参考轨迹。我们在真实森林环境与仿真森林环境中全面验证了系统性能，这些环境具有超过400棵/公顷的高树木密度，飞行速度最高达3米/秒，且未发生任何碰撞或系统故障。据我们所知，这是首个在如此非结构化环境中使用低成本被动视觉传感器及包含VI-SLAM的完全机载计算达到该性能水平的系统。