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

arXiv: https://arxiv.org/abs/2301.07430 · HTML: https://mlsanity.com/paper/2301.07430/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance](https://mlsanity.com/paper/2405.10391/) (2024)
- [Neuromorphic Attitude Estimation and Control](https://mlsanity.com/paper/2411.13945/) (2024)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)
- [MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots](https://mlsanity.com/paper/2606.07813/) (2026)

## Abstract (arXiv)

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.