Real-time Identification and Simultaneous Avoidance of Static and Dynamic Obstacles on Point Cloud for UAVs Navigation

TL;DR

This paper presents a real-time method for UAVs to identify, distinguish, and avoid static and dynamic obstacles using point cloud data, enhancing navigation safety and efficiency.

Contribution

It introduces a novel approach combining obstacle classification, forbidden pyramids for dynamic avoidance, and techniques for handling estimation errors, all optimized for onboard real-time operation.

Findings

Robust obstacle tracking and classification in real-time

Efficient dynamic avoidance with forbidden pyramids

Validated improvements in energy cost and calculation time

Abstract

Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermore, we present several techniques to deal with the position estimation error for close objects, the error for deformable objects, and the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and hardware tests, demonstrating our superiority in tracking robustness, energy cost, and calculating time.