外观
论文arXiv 2608.170382 个月前
面向自主 UGV 导航的融合全局 DEM 数据的地形感知局部路径规划
原文链接：Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation（arXiv 2608.17038 · 发布日期：2026-08-17）
Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation
针对复杂户外地形中全局地图与实时感知脱节问题，提出融合 DEM 与 LiDAR 的混合规划框架，并在 Gazebo 仿真验证。
核心要点
- 动机是解决 UGV 在复杂户外地形中全局制图与实时传感反馈不一致带来的导航挑战。
- 方法先用预处理 DEM 基于 A* 计算全局路径，再由实时 LiDAR 障碍检测与地形分析进行局部修正。
- Gazebo 仿真显示，相比基线方法，障碍避让率达到 95%，平均遭遇坡度由 8° 降至 2.7°。
- 该集成提升路径效率与地形可通行性，并支持对突发环境变化的实时自适应。
验证范围与局限：验证范围为 Gazebo 中自定义地形的仿真实验。
英文摘要 (Abstract)
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.
作者：Singh, Devender, Issah Suleiman, Paul Mitten, Glenn Cutler, Vinicius Prado da Fonseca, Matthew Hamilton
来源
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