AvoidBench

A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors

Conference paper (2023)
Copyright: © 2023 H.Y. Yu, G.C.H.E. de Croon, C. de Wagter
DOI: https://doi.org/10.1109/ICRA48891.2023.10161097
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we propose AvoidBench, a benchmarking suite which can evaluate the performance of vision-based obstacle avoidance algorithms by subjecting them to a series of tasks. Thanks to the high fidelity of multi-rotors dynamics from RotorS and virtual scenes of Unity3D, AvoidBench can realize realistic simulated flight experiments. Compared to current drone simulators, we propose and implement both performance and environment metrics to reveal the suitability of obstacle avoidance algorithms for environments of different complexity. To illustrate AvoidBench's usage, we compare three algorithms: Ego-planner, MBPlanner, and Agile-autonomy. The trends observed are validated with real-world obstacle avoidance experiments. Code is available at: https://github.com/tudelft/AvoidBench

Files

AvoidBench_A_high_fidelity_vis... (pdf)
(pdf | 4.47 Mb)
- Embargo expired in 05-02-2024