发表机构
Memorial University of Newfoundland; Compusult Ltd.（纽芬兰纪念大学; 康普萨尔特有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对UGV户外导航中全局地图与实时传感器反馈脱节的问题，提出融合DEM与LiDAR的混合路径规划框架，经仿真验证可提升障碍物规避率、降低平均坡度，为可靠自主导航提供支撑。
AI 中文摘要
复杂户外地形中的自主导航对无人地面车辆（UGV）构成关键挑战，原因在于全局地图与实时传感器反馈之间存在固有脱节。本研究提出一种混合框架，将低分辨率数字高程模型（DEM）数据与基于实时激光雷达（LiDAR）的障碍物检测及地形分析相融合，以实现高效路径规划。首先，使用经预处理的基于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在应对突发环境变化的同时保持安全与效率。在Gazebo中的仿真结果显示，与基线方法相比取得显著提升：在自定义地形中实现95%的障碍物规避率，将平均遭遇坡度从8°降至2.7°。该融合方案提升了路径效率与地形可通行性，支持稳健的实时适配，为动态户外环境中更可靠的自主导航铺平道路。
英文摘要
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.
