An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision

TL;DR

This paper introduces a new image-based path planning algorithm for UAVs using stereo vision and computer vision techniques, demonstrating its effectiveness through simulation and laboratory experiments.

Contribution

It develops a novel stereo vision-based path planning method that automatically detects start and end points and compares favorably with established algorithms.

Findings

The proposed algorithm effectively plans paths in virtual and physical environments.

Stereo disparity maps improve path safety assessment.

The method outperforms traditional algorithms in certain scenarios.

Abstract

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.