Shen, Na (Karlsruhe Insitute of Technology), Cao, Jiale (Karlsruhe Insitute of Technology), Zipp, Matthias (Karlsruhe Insitute of Technology), Stork, Wilhlem (Karlsruhe Insitute of Technology)

Autonomous Obstacle Avoidance for UAV Based on Point Cloud

Scheduled for presentation during the Regular Session "Autonomy" (FrC4), Friday, June 24, 2022, 16:40−17:00, Divona-2

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

This paper presents an autonomous obstacle detection, avoidance, and path-finding algorithm for UAV (unmanned aerial vehicle) in outdoor applications by using a stereo depth camera. The obstacle avoidance part is divided into two steps: process the point cloud, remove noise and points of ground, and extract clusters to represent different objects; perform and define detectors, to detect whether the UAV has a collision at different distances and in various directions around the nearest obstacle, even behind the free escape space respectively. Finally, the flight decision is selected with the direction with highest votes and publish the flight command to be executed by UAV. A voting mechanism for judgments during a period is implemented, to avoid the misjudgment by a single frame, as well as to enhance the plausibility of flight decision. The algorithm is evaluated in Gazebo-based simulation environment as well as in field tests, has been approved that the UAV can successfully avoid obstacles in dense woods and find new obstacle-free paths, even in extreme environments with huge buildings, which indicated that the algorithm achieved its initial purpose.