Robust Localization in Large-Scale Symmetric Environments through Dynamic Topological Mapping
Rafael Flor RodrÃguez-Rabadán, Sergio Lafuente-Arroyo, Saturnino Maldonado-BascÃ3n, Carlos Gutiérrez Ãlvarez, Roberto J. LÃ3pez-Sastre
AI summary
Problem
Visual place recognition degrades in large-scale indoor settings due to perceptual aliasing from structural symmetry, ambiguous open areas, and dynamic environmental changes that break static maps.
Approach
The system builds a two-layer scene graph that merges LiDAR-derived structural layouts with RGB sequence descriptors, using edge-weight attenuation to detect topological changes and trigger autonomous map updates.
Key results
- Zero localization error across diverse real-world conditions
- Automatic detection and mapping of dynamic topological changes
- Scalable performance in large, symmetric environments
- SuperGlue maximizes accuracy while SIFT offers optimal cost-accuracy trade-off
Why it matters
Enables reliable, lifelong robot autonomy in complex public spaces without manual map maintenance.
Abstract
Visual place recognition in large-scale, indoor en- vironments often suffers from perceptual aliasing due to struc- tural symmetries and dynamic changes. This work presents a robust hierarchical topological mapping framework designed for long-term robot autonomy. Our system integrates multi- modal data (including 2D LiDAR, odometry, and RGB imagery) into a two-layer architecture. First, a Layout Layer is designed to capture the geometric structure of the environment. Then, a Visual Layer is used to encode image sequences. A key con- tribution is the dynamic map maintenance mechanism, which monitors the attenuation of edge weights to detect environmen- tal transitions, such as the opening or closing of doors. This allows for seamless lifelong updates without human intervention in large-scale environments. We evaluate our approach using various visual descriptors (e.g. SuperGlue, Patch-NetVLAD, and SeqVLAD) within a sequence-based matching pipeline. Experimental results in a 750 m2 real-world facility demonstrate that the proposed method achieves high discrimination and scalability, even in challenging open areas and symmetric corridors. This framework provides a reliable solution for assistive robotics navigating complex, evolving public spaces.