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

2026-05-08

AI Summary

This work addresses the challenge of deploying conventional dense-mapping-based exploration methods on micro aerial vehicles, which are severely constrained by size, weight, and power limitations. To overcome this, the authors propose a lightweight autonomous exploration system that integrates omnidirectional vision from multiple fisheye cameras with monocular depth estimation to construct a sparse topological map. Frontier regions are implicitly represented by topological nodes, thereby eliminating the need for explicit maintenance of a dense occupancy map. Global path planning is performed directly on this sparse graph. The approach is validated both in simulation and on a real palm-sized drone with an 11-cm wheelbase and a mass of 400 grams, demonstrating efficient autonomous exploration while substantially reducing memory footprint and computational overhead.

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.

Problem

• UAV exploration
• sparse topological map
• omnidirectional vision
• SWaP constraints
• frontier detection

Innovation

• omnidirectional vision
• sparse topological map
• frontier exploration
• micro UAV
• lightweight autonomous navigation