# Autonomous Flights in Dynamic Environments with Onboard Vision

arXiv: https://arxiv.org/abs/2103.05870 · HTML: https://mlsanity.com/paper/2103.05870/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 8 of 8)

- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [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)
- [Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments](https://mlsanity.com/paper/2209.14233/) (2022)
- [ODTFormer: Efficient Obstacle Detection and Tracking with Stereo Cameras Based on Transformer](https://mlsanity.com/paper/2403.14626/) (2024)
- [Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter](https://mlsanity.com/paper/2607.23565/) (2026)

## Abstract (arXiv)

In this paper, we introduce a complete system for autonomous flight of quadrotors in dynamic environments with onboard sensing. Extended from existing work, we develop an occlusion-aware dynamic perception method based on depth images, which classifies obstacles as dynamic and static. For representing generic dynamic environment, we model dynamic objects with moving ellipsoids and fuse static ones into an occupancy grid map. To achieve dynamic avoidance, we design a planning method composed of modified kinodynamic path searching and gradient-based optimization. The method leverages manually constructed gradients without maintaining a signed distance field (SDF), making the planning procedure finished in milliseconds. We integrate the above methods into a customized quadrotor system and thoroughly test it in realworld experiments, verifying its effective collision avoidance in dynamic environments.