Problem
Research questions and friction points this paper is trying to address.
Autonomous Navigation
UGVs
Complex Outdoor Terrains
Global Mapping
Real-time Sensor Feedback
Terrain-Aware Local Path Planning with Global DEM Data Integration for Autonomous UGV Navigation
🤖 AI Summary
该研究通过结合DEM数据与实时LiDAR检测，提出了一种混合框架以解决UGV在复杂户外地形中的自主导航问题，提高了路径规划效率和安全性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Digital Elevation Model (DEM)
LiDAR-based obstacle detection
adaptive path correction
real-time adaptation
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