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

2022-10-17

Annual Conference of the IEEE Industrial Electronics Society

AI Summary

Binocular-vision UAVs suffer from insufficient path safety in complex 3D terrains due to the inherent lack of depth information in 2D images. Method: This paper proposes an image-driven path planning method integrating stereo vision with multi-feature detection. A disparity map is constructed to recover terrain depth; safe waypoints are automatically extracted via joint detection of edges, line segments, and corners; and ArUco markers enable pose estimation for start/end-point identification and trajectory generation. Contribution/Results: The method innovatively synergizes depth reconstruction with heterogeneous image features for robust waypoint selection. Evaluated in V-REP simulation and on a physical UAV platform, it significantly improves path safety and terrain adaptability compared to conventional A* and PRM algorithms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops UAV stereo vision path planning using disparity maps
• Compares algorithm with A-star and PRM in simulations
• Automates waypoint detection through computer vision techniques

Innovation

• UAV stereo vision generates terrain disparity map
• Computer vision techniques define trajectory way-points
• ArUco markers enable automatic start-end point detection