融合地形图与实时传感器数据，让无人车更安全高效地穿越复杂户外地形。
Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation
- 用低分辨率地形图预生成全局路径，再结合激光雷达数据动态修正局部路径。
- 在自定义地形中实现95%的障碍物避让率，平均坡度从8°降至2.7°。
- 适合需要高可靠性的户外无人车导航系统，尤其在动态变化环境中。
无人地面车辆（UGVs）在复杂户外地形中的自主导航面临重大挑战，源于全局地图与实时传感器反馈之间的脱节。本文提出一种混合框架，将低分辨率数字高程模型（DEM）数据与实时激光雷达障碍物检测和地形分析相结合，实现高效路径规划。首先使用预处理的基于DEM的A*算法计算全局路径；随后，局部传感器数据驱动自适应路径修正，使UGV能够应对突发环境变化，同时保障安全与效率。在Gazebo中的仿真结果表明，相比基线方法，该方案在自定义地形中实现了95%的障碍物避让率，并将平均遭遇坡度从8°降低至2.7°。该集成显著提升了路径效率与地形可通过性，支持鲁棒的实时适应，为动态户外环境中更可靠的自主导航奠定了基础。
原文摘要 · Abstract (English)
Autonomous navigation in complex outdoor terrains presents critical challenges for unmanned ground vehicles (UGVs) due to the inherent disconnect between global mapping and real-time sensor feedback. This work proposes a hybrid framework that integrates low-resolution Digital Elevation Model (DEM) data with real-time LiDAR-based obstacle detection and terrain analysis for efficient path planning. A global path is initially computed using a preprocessed DEM-based A* algorithm. Subsequently, local sensor data drives adaptive path correction, enabling the UGV to negotiate sudden environmental changes while maintaining safety and efficiency. Simulation results in Gazebo demonstrate significant improvements over a baseline approach, achieving a 95\% obstacle avoidance rate and reducing the average encountered slope from $8^\circ$ to $2.7^\circ$ in custom terrain. This integration enhances path efficiency and terrain traversability and supports robust real-time adaptation, paving the way for more reliable autonomous navigation in dynamic outdoor environments.
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