下倾激光雷达+算法协同设计，提升无人机在废墟中的定位与避障能力。
EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR
- 下倾20°激光雷达捕获密集地面点云，增强环境感知与定位约束。
- 实测在室内外场景中跟踪误差降低81%，垂直漂移接近零。
- 适合灾害搜救等复杂环境下无人机自主导航，开源可复现。
为解决无人飞行器在开放顶部场景（如倒塌建筑、无顶迷宫）中面临的定位漂移与感知-规划耦合问题，本文提出EAROL框架，采用下倾20°安装的激光雷达配置，集成激光雷达-惯性里程计（LIO）系统与分层轨迹-航向优化算法。硬件创新通过密集地面点云获取和前方环境感知，提升动态障碍物检测能力。基于动态运动补偿的迭代误差状态卡尔曼滤波器（IESKF）实现紧密耦合的LIO系统，在特征稀疏环境中达到高精度6-DoF定位。规划器融合环境信息，平衡探索、目标追踪精度与能效。物理实验表明，在室内迷宫及60米级室外场景中，跟踪误差减少81%，感知覆盖提升22%，垂直漂移近乎为零。本工作提出软硬件协同设计范式，为灾后搜救任务中的无人机自主提供鲁棒解决方案。代码与硬件设计将开源共享。视频：https://youtu.be/7av2ueLSiYw。
原文摘要 · Abstract (English)
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° 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.
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