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

TL;DR

This paper introduces a lightweight, vision-based UAV exploration system using omnidirectional cameras and sparse topological maps, enabling efficient, low-resource environment exploration suitable for micro UAVs.

Contribution

It presents a novel exploration approach that replaces dense occupancy maps with sparse topological representations, reducing memory and computational requirements for micro UAVs.

Findings

Successful real-world experiments on a palm-sized UAV demonstrate efficient exploration.

The method achieves low computational consumption suitable for SWaP-constrained UAVs.

Sparse topological maps effectively guide exploration without dense environmental data.

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.