中文标题#
EAROL：通过向下安装的倾斜激光雷达实现环境增强感知感知规划和鲁棒里程计
英文标题#
EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR
中文摘要#
为解决在开放顶部场景（如坍塌的建筑物、无顶迷宫）中运行的无人机（UAV）面临的定位漂移和感知 - 规划耦合挑战，本文提出了 EAROL，一种新型框架，采用向下安装的倾斜激光雷达配置（20 {\deg} 倾斜角），集成了激光雷达惯性里程计（LIO）系统和分层轨迹偏航优化算法。硬件创新通过密集地面点云获取和前方环境感知，实现了约束增强和动态障碍物检测。一个由具有动态运动补偿的迭代误差状态卡尔曼滤波器（IESKF）增强的紧密耦合 LIO 系统，在特征稀疏环境中实现了高水平的 6-DoF 定位精度。规划器通过环境增强，平衡环境探索、目标跟踪精度和能效。物理实验表明，在室内迷宫和 60 米尺度的户外场景中，跟踪误差减少了 81%，感知覆盖提高了 22%，垂直漂移接近零。这项工作提出了一种软硬件协同设计范式，为灾后搜索和救援任务中的无人机自主性提供了稳健的解决方案。我们将发布我们的软件和硬件作为开源包供社区使用。视频：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：通过向下安装的倾斜激光雷达实现环境增强感知感知规划和鲁棒里程计
PDF 获取#
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