Topological Mapping and Navigation using a Monocular Camera based on AnyLoc

TL;DR

This paper introduces a monocular camera-based topological mapping and navigation method using AnyLoc, enabling efficient loop detection and navigation without pre-training, suitable for robots and humans.

Contribution

It presents a novel topological mapping approach based on keyframe descriptors from AnyLoc, improving success rates and reducing computational costs compared to existing methods.

Findings

Achieves effective loop detection in real and simulated environments.

Improves navigation success rates by 60.2% over ResNet-based methods.

Reduces time and space costs for map building and navigation.

Abstract

This paper proposes a method for topological mapping and navigation using a monocular camera. Based on AnyLoc, keyframes are converted into descriptors to construct topological relationships, enabling loop detection and map building. Unlike metric maps, topological maps simplify path planning and navigation by representing environments with key nodes instead of precise coordinates. Actions for visual navigation are determined by comparing segmented images with the image associated with target nodes. The system relies solely on a monocular camera, ensuring fast map building and navigation using key nodes. Experiments show effective loop detection and navigation in real and simulation environments without pre-training. Compared to a ResNet-based method, this approach improves success rates by 60.2% on average while reducing time and space costs, offering a lightweight solution for robot and human navigation in various scenarios.