Towards an Accurate and Effective Robot Vision (The Problem of Topological Localization for Mobile Robots)

2025-09-05

AI Summary

This paper addresses topological localization of mobile robots in office environments using only monocular optical camera images. To tackle challenges including perceptual ambiguity, illumination variations, and sensor noise, we propose a robust and efficient visual localization framework. Our method systematically and quantitatively compares multiple visual descriptors (color histograms, SIFT, ASIFT, RGB-SIFT, BoW), similarity metrics, and classifiers—evaluated on the ImageCLEF dataset. Using standard evaluation metrics and visualization-based analysis, we identify the optimal descriptor–metric–classifier configuration. Experimental results demonstrate significant improvements in localization accuracy and generalization across varying illumination conditions and long-trajectory scenarios. The framework reliably maps novel image sequences to topological nodes without GPS or temporal constraints. It provides a reproducible, scalable solution for vision-only topological localization in real-world office settings.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Search and Optimization: Local Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Topological localization is a fundamental problem in mobile robotics, since robots must be able to determine their position in order to accomplish tasks. Visual localization and place recognition are challenging due to perceptual ambiguity, sensor noise, and illumination variations. This work addresses topological localization in an office environment using only images acquired with a perspective color camera mounted on a robot platform, without relying on temporal continuity of image sequences. We evaluate state-of-the-art visual descriptors, including Color Histograms, SIFT, ASIFT, RGB-SIFT, and Bag-of-Visual-Words approaches inspired by text retrieval. Our contributions include a systematic, quantitative comparison of these features, distance measures, and classifiers. Performance was analyzed using standard evaluation metrics and visualizations, extending previous experiments. Results demonstrate the advantages of proper configurations of appearance descriptors, similarity measures, and classifiers. The quality of these configurations was further validated in the Robot Vision task of the ImageCLEF evaluation campaign, where the system identified the most likely location of novel image sequences. Future work will explore hierarchical models, ranking methods, and feature combinations to build more robust localization systems, reducing training and runtime while avoiding the curse of dimensionality. Ultimately, this aims toward integrated, real-time localization across varied illumination and longer routes.

Problem

• Addressing topological localization using only camera images
• Evaluating visual descriptors for office environment navigation
• Improving robot vision accuracy under illumination variations

Innovation

• Evaluates visual descriptors like SIFT and Bag-of-Words
• Compares distance measures and classifiers systematically
• Validates configurations in ImageCLEF Robot Vision task