# Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments

arXiv: https://arxiv.org/abs/2002.04920 · HTML: https://mlsanity.com/paper/2002.04920/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud](https://mlsanity.com/paper/2309.09737/) (2023)
- [DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles](https://mlsanity.com/paper/2109.07024/) (2021)
- [A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs](https://mlsanity.com/paper/2311.12893/) (2023)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera](https://mlsanity.com/paper/2209.08258/) (2022)
- [A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles](https://mlsanity.com/paper/2301.08422/) (2023)
- [BatMobility: Towards Flying Without Seeing for Autonomous Drones](https://mlsanity.com/paper/2307.11518/) (2023)
- [Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments](https://mlsanity.com/paper/2402.16449/) (2024)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments](https://mlsanity.com/paper/2209.14233/) (2022)
- [Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter](https://mlsanity.com/paper/2607.23565/) (2026)

## Abstract (arXiv)

In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles. Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold. The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties. The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.