AvoidBench：マルチロータ向け高忠実度視覚ベース障害物回避ベンチマークスイート
AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors
RotorSの高忠実度マルチロータ動力学とUnity3Dの仮想環境を組み合わせ、視覚ベース障害物回避アルゴリズムを性能・環境指標で評価するベンチマークを提案し、3手法を比較して実機実験で傾向を検証した。
著者: Hang Yu, Guido C. H. E de Croon, Christophe De Wagter
分類: cs.RO
原文アブストラクト
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.
