# MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots

arXiv: https://arxiv.org/abs/2606.07813 · HTML: https://mlsanity.com/paper/2606.07813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation](https://mlsanity.com/paper/1805.09806/) (2018)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [AutoFlow: Learning a Better Training Set for Optical Flow](https://mlsanity.com/paper/2104.14544/) (2021)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation](https://mlsanity.com/paper/1808.01838/) (2018)
- [EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://mlsanity.com/paper/1906.02919/) (2019)
- [Autonomous Drone Racing: A Survey](https://mlsanity.com/paper/2301.01755/) (2023)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes](https://mlsanity.com/paper/1904.03848/) (2019)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor](https://mlsanity.com/paper/2011.00534/) (2020)
- [End-to-end Neural Network Based Quadcopter control](https://mlsanity.com/paper/2304.13460/) (2023)
- [NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter](https://mlsanity.com/paper/2209.06918/) (2022)
- [AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors](https://mlsanity.com/paper/2301.07430/) (2023)
- [GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight](https://mlsanity.com/paper/1802.05330/) (2018)
- [Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots](https://mlsanity.com/paper/2307.11522/) (2023)
- [MorphEyes: Variable Baseline Stereo For Quadrotor Navigation](https://mlsanity.com/paper/2011.03077/) (2020)
- [NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction](https://mlsanity.com/paper/2109.13859/) (2021)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots](https://mlsanity.com/paper/2411.14576/) (2024)

## Abstract (arXiv)

Navigation using a monocular camera is pivotal for autonomous operation on tiny aerial robots due to their perfect balance of versatility, cost and accuracy. In this paper, we introduce MinNav, a navigation stack based on optical flow and its uncertainty to fly through a scene with static and dynamic obstacles and unknown-shaped gaps without any prior knowledge of the scene components and/or their locations/ordering. We further improve success rate by using the activeness of the robot to move around in an exploratory way to find obstacles and navigate. We successfully evaluate and demonstrate the proposed approach in many real-world experiments in various environments with static and dynamic obstacles and unknown-shaped gaps with an overall success rate of 70%. To the best of our knowledge, this is the first solution to tackle all the aforementioned navigation cases without prior knowledge using a monocular camera. Our approach is on par in performance with depth based methods with factors of magnitude less computation required and can readily run onboard tiny aerial robots. The accompanying video, supplementary material, code and dataset can be found at https://pear.wpi.edu/research/minnav.html