Dynamic Obstacle Detection and Environmental Modelling for Agricultural Drones

Abstract

Drones used in precision agricultural applications are required to fly at low altitudes where a multitude of static and dynamic obstacles are present. Real-time perception and obstacle detection are crucial to achieve collision avoidance accurately and speedily in a cluttered and dynamic environment with multiple static and moving obstacles. This paper presents a real-time obstacle detection and environmental modelling method for agricultural drones in the presence of multiple static and dynamic obstacles. A simulation experimental test bed was setup and the results of the evaluation experiments verified the capability of the proposed ellipsoidal bounding box approximation-based method for representing the environment properly with a significant reduction in volume for real-world obstacles in comparison to existing benchmark spherical bounding box approximation. Furthermore, the proposed ellipsoidal bounding box approximation method continues to perform better over benchmark spherical bounding box approximation when point clouds keep getting larger as the industry develops perception sensors of higher quality and performance.