利用航空激光雷达地图进行林冠下导航
Under-Canopy Navigation using Aerial Lidar Maps
AI总结：
针对无先验信息的林下目标导航，本文提出用航空激光雷达构建含轨迹不确定性的三维概率占据地图，并设计融合路径长度与障碍风险的D-Star Lite规划及动态重规划方法，实验验证其有效性。
摘要：
在非结构化自然环境中的自主导航构成了一项重大挑战。在没有先验信息的目标导航任务中，机器人所使用的机载传感器前视距离有限，这会损害路径效率。我们提出一种新颖方法，利用林冠上方的航空地图来改进地面机器人导航。我们的系统利用航空激光雷达扫描创建三维概率占据地图，并独特地纳入飞行器轨迹的不确定性以提高精度。文中引入了新颖的路径规划代价函数，将路径长度与根据概率地图估计的障碍风险相结合。随后，D-Star Lite算法计算通往目标的最优（最小代价）路径。该系统还允许在地面遇到未预见障碍物时进行动态重规划。在模拟森林和真实森林中开展的大量实验与消融研究证明了本系统的有效性。
英文摘要：
Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aerial map for improved ground robot navigation. Our system utilises aerial lidar scans to create a 3D probabilistic occupancy map, uniquely incorporating the uncertainty in the aerial vehicle's trajectory for improved accuracy. Novel path planning cost functions are introduced, combining path length with obstruction risk estimated from the probabilistic map. The D-Star Lite algorithm then calculates an optimal (minimum-cost) path to the goal. This system also allows for dynamic replanning upon encountering unforeseen obstacles on the ground. Extensive experiments and ablation studies in simulated and real forests demonstrate the effectiveness of our system.
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。
