An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision
This work addresses path planning for UAVs in complex terrains, but it is incremental as it builds on existing computer vision and planning methods.
The paper tackles the problem of safe path planning for UAVs in terrain with craters and hills by using stereo vision to generate a disparity map for 3D obstacle detection, and shows promising results in simulation and physical tests compared to A* and PRM algorithms.
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.