MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots
MinNav is a navigation stack based on optical flow and its uncertainty, enabling tiny aerial robots to fly through scenes with static/dynamic obstacles and unknown-shaped gaps without prior knowledge. It achieves a 70% success rate in real-world experiments and matches depth-based methods with far less computation.
[2606.07813] MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots
[Submitted on 5 Jun 2026]
Title:MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots
View a PDF of the paper titled MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots, by Aniket Patil and 3 other authors
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Abstract: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 this https URL
Comments: Accepted for publication at ICRA 2026. Link to Project page this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.07813 [cs.RO]
(or arXiv:2606.07813v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.07813
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aniket Patil [view email] [v1] Fri, 5 Jun 2026 19:45:02 UTC (40,560 KB)
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