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Obstacle avoidance is an essential topic in the field of autonomous droneresearch. When choosing an avoidance algorithm, many different options areavailable, each with their advantages and disadvantages. As there is currentlyno consensus on testing methods, it is quite challenging to compare theperformance between algorithms. In this paper, we propose AvoidBench, abenchmarking suite which can evaluate the performance of vision-based obstacleavoidance algorithms by subjecting them to a series of tasks. Thanks to thehigh fidelity of multi-rotors dynamics from RotorS and virtual scenes ofUnity3D, AvoidBench can realize realistic simulated flight experiments.Compared to current drone simulators, we propose and implement both performanceand environment metrics to reveal the suitability of obstacle avoidancealgorithms for environments of different complexity. To illustrate AvoidBench'susage, we compare three algorithms: Ego-planner, MBPlanner, and Agile-autonomy.The trends observed are validated with real-world obstacle avoidanceexperiments.

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