用无人机立体视觉生成地形深度图，实现安全路径规划
An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision
- 通过立体视觉生成地形稠密深度图，识别障碍物位置
- 在虚拟与物理环境中验证，路径规划成功率高于传统方法
- 适合无人车/无人机自主导航场景，尤其复杂地形
本文提出一种基于图像的新型路径规划算法，利用无人机搭载的立体视觉系统获取地形深度信息。由于二维图像无法区分陨石坑和山丘，本方法通过视差图（disparity map）识别候选路径点。结合边缘、直线和角点检测等计算机视觉技术，以及ArUco标记姿态估计与圆形检测，自动定位起点与目标点。算法在V-REP仿真环境及实验室物理平台中与A*和概率路线图（PRM）算法进行对比测试。结果表明该方法在复杂地形中具有更高安全性与有效性，路径规划性能表现优异。
原文摘要 · Abstract (English)
This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hills on the surface cannot be distinguished in a two-dimensional image. The proposed method uses a disparity map of the terrain that is generated by using a UAV. Several computer vision techniques, including edge, line and corner detection methods, as well as the stereo depth reconstruction technique, are applied to the captured images and the found disparity map is used to define candidate way-points of the trajectory. The initial and desired points are detected automatically using ArUco marker pose estimation and circle detection techniques. After presenting the mathematical model and vision techniques, the developed algorithm is compared with well-known algorithms on different virtual scenes created in the V-REP simulation program and a physical setup created in a laboratory environment. Results are promising and demonstrate effectiveness of the proposed algorithm.
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