让无人机路径规划感知地形，提升复杂环境三维重建效果
Terrain-Aware Adaptation for Two-Dimensional UAV Path Planners
- 将二维路径规划模块扩展为可调整高度与相机朝向的地形感知系统
- 在多个3D场景和真实飞行测试中，垂直结构区域重建精度显著提升
- 适合需要高精度三维建模的无人机巡检、测绘等应用
主流商业软件中的多无人机覆盖路径规划（mCPP）算法通常将感兴趣区域（RoI）视为二维平面，忽略重要的三维结构特征，导致在遮挡或垂直表面附近重建不完整。本文提出一种模块化算法，可将现有二维路径规划器扩展为支持地形感知的规划方法，通过动态调整飞行高度和相机姿态实现优化。我们以经典的DARP算法为基础，构建了DARP-3D。在多个三维环境中的仿真结果以及使用DJI设备的真实飞行测试表明，相比基线方法，本方案在具有显著垂直特征的区域实现了更完整的三维重建。算法已开源：https://github.com/konskara/TerraPlan
原文摘要 · Abstract (English)
Multi-UAV Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important3D structure characteristics. This leads to incomplete 3Dreconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two-dimensional path planners to facilitate terrain-aware planning by adjusting altitude and camera orientations. To demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. We present simulation results in multiple 3D environments and a real-world flight test using DJI hardware. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open-source implementation of the algorithm is available here:https://github.com/konskara/TerraPlan
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