# Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization

arXiv: https://arxiv.org/abs/2209.07003 · HTML: https://mlsanity.com/paper/2209.07003/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots](https://mlsanity.com/paper/1903.02144/) (2019)
- [A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application](https://mlsanity.com/paper/1605.02406/) (2016)
- [DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles](https://mlsanity.com/paper/2109.07024/) (2021)
- [Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap](https://mlsanity.com/paper/2010.07429/) (2020)
- [Dynamic Occupancy Grid Mapping with Recurrent Neural Networks](https://mlsanity.com/paper/2011.08659/) (2020)
- [Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments](https://mlsanity.com/paper/2002.04920/) (2020)
- [Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking](https://mlsanity.com/paper/2007.10743/) (2020)
- [Autonomous Flights in Dynamic Environments with Onboard Vision](https://mlsanity.com/paper/2103.05870/) (2021)
- [Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments](https://mlsanity.com/paper/2201.06645/) (2022)

## Built on by (most cited first, 9 of 9)

- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [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)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments](https://mlsanity.com/paper/2410.02732/) (2024)
- [Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs](https://mlsanity.com/paper/2507.21506/) (2025)
- [Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter](https://mlsanity.com/paper/2607.23565/) (2026)
- [Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion](https://mlsanity.com/paper/2608.28665/) (2026)
- [PerSeM: Persistent Semantic Memory for Long-Horizon Open-Vocabulary UAV Mapping](https://mlsanity.com/paper/2609.19542/) (2026)

## Abstract (arXiv)

Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation, such as the geometric, occupancy, or ESDF map. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, this paper proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot's onboard vision. The depth vision enables the robot to track and represent dynamic objects geometrically based on the voxel map. The proposed optimization first adopts the circle-based guide-point algorithm to approximate the costs and gradients for avoiding static obstacles. Then, with the vision-detected moving objects, our receding-horizon distance field is simultaneously used to prevent dynamic collisions. Finally, the iterative re-guide strategy is applied to generate the collision-free trajectory. The simulation and physical experiments prove that our method can run in real-time to navigate dynamic environments safely. Our software is available on GitHub as an open-source package.