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Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance

Summary

This paper proposes a lightweight autonomous exploration system using omnidirectional vision and sparse topological maps for micro UAVs. It employs multi-fisheye cameras for full FoV and depth estimation, represents frontiers as topological nodes, and achieves efficient exploration with low memory and computation. Validated on a palm-sized UAV (11cm wheelbase, 400g weight).

SourcearXiv RoboticsAuthor: Zirui Wang, Xinjia Luo, Haotian Sun, Jun Ma, Jian Guo, Boyu Zhou
Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance
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[2605.07275] Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance

[Submitted on 8 May 2026]

Title:Palm-sized Omnidirectional Vision-Based UAV Exploration with Sparse Topological Map Guidance

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Abstract:Classic exploration methods often rely on dense occupancy maps or high-resolution point clouds for frontier detection and path planning, resulting in substantial memory consumption and computational overhead. Moreover, micro UAVs under size, weight, and power (SWaP) constraints are not practical to be equipped with sensors like LiDAR to obtain accurate environmental geometric measurements. This paper presents a lightweight autonomous exploration system that leverages omnidirectional vision and sparse topological map guidance. Specifically, we utilize a multi-fisheye camera setup to achieve omnidirectional Field of View (FoV) and perform depth estimation. To address the limited depth estimation accuracy, frontiers are represented as potential unexplored regions characterized by topological nodes instead of explicit boundaries, enabling efficient identification of frontier regions without maintaining occupancy grids or global point clouds. Unlike classic dense representations, our approach abstracts the environment using a sparse topological map composed of key nodes and their descriptors, reducing memory consumption and computational demands. Global path planning is performed directly on the sparse graph. The proposed method is validated in both simulation and on a palm-sized vision-based UAV with an 11 cm wheelbase and a 400 g weight in real-world experiments, demonstrating that our method can achieve efficient exploration with extremely low computational consumption.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.07275 [cs.RO]

(or arXiv:2605.07275v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.07275

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zirui Wang [view email] [v1] Fri, 8 May 2026 05:40:20 UTC (5,035 KB)

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Key points

  • Conventional methods rely on dense occupancy maps or point clouds, which are memory- and computation-intensive for micro UAVs.
  • Multi-fisheye cameras provide omnidirectional depth estimation; frontiers are represented as topological nodes.
  • A sparse topological map of key nodes and descriptors is used for global path planning directly.
  • Real-world tests on a palm-sized UAV (11cm wheelbase, 400g) show extremely low computational consumption.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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