中文标题#
EAROL：通過向下安裝的傾斜激光雷達實現環境增強感知感知規劃和魯棒里程計
英文标题#
EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR
中文摘要#
為解決在開放頂部場景（如坍塌的建築物、無頂迷宮）中運行的無人機（UAV）面臨的定位漂移和感知 - 規劃耦合挑戰，本文提出了 EAROL，一種新型框架，採用向下安裝的傾斜激光雷達配置（20 {\deg} 傾斜角），集成了激光雷達慣性里程計（LIO）系統和分層軌跡偏航優化算法。硬體創新通過密集地面點雲獲取和前方環境感知，實現了約束增強和動態障礙物檢測。一個由具有動態運動補償的迭代誤差狀態卡爾曼濾波器（IESKF）增強的緊密耦合 LIO 系統，在特徵稀疏環境中實現了高水平的 6-DoF 定位精度。規劃器通過環境增強，平衡環境探索、目標跟蹤精度和能效。物理實驗表明，在室內迷宮和 60 米尺度的戶外場景中，跟蹤誤差減少了 81%，感知覆蓋提高了 22%，垂直漂移接近零。這項工作提出了一種軟硬體協同設計範式，為災後搜索和救援任務中的無人機自主性提供了穩健的解決方案。我們將發布我們的軟體和硬體作為開源包供社區使用。視頻：https://youtu.be/7av2ueLSiYw.
英文摘要#
To address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UAVs) operating in open-top scenarios (e.g., collapsed buildings, roofless mazes), this paper proposes EAROL, a novel framework with a downward-mounted tilted LiDAR configuration (20{\deg} inclination), integrating a LiDAR-Inertial Odometry (LIO) system and a hierarchical trajectory-yaw optimization algorithm. The hardware innovation enables constraint enhancement via dense ground point cloud acquisition and forward environmental awareness for dynamic obstacle detection. A tightly-coupled LIO system, empowered by an Iterative Error-State Kalman Filter (IESKF) with dynamic motion compensation, achieves high level 6-DoF localization accuracy in feature-sparse environments. The planner, augmented by environment, balancing environmental exploration, target tracking precision, and energy efficiency. Physical experiments demonstrate 81% tracking error reduction, 22% improvement in perceptual coverage, and near-zero vertical drift across indoor maze and 60-meter-scale outdoor scenarios. This work proposes a hardware-algorithm co-design paradigm, offering a robust solution for UAV autonomy in post-disaster search and rescue missions. We will release our software and hardware as an open-source package for the community. Video: https://youtu.be/7av2ueLSiYw.
文章页面#
EAROL：通過向下安裝的傾斜激光雷達實現環境增強感知感知規劃和魯棒里程計
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