OpenNavMap: Structure-Free Topometric Mapping via Large-Scale Collaborative Localization

2026-01-18

AI Summary

This work addresses the limitations of traditional structured mapping methods, which often fail under feature scarcity or drastic viewpoint changes and struggle to scale to large-scale collaborative scenarios. The authors propose a lightweight, structure-free topometric mapping system that eliminates reliance on pre-built 3D models and, for the first time, enables large-scale collaborative localization and globally consistent mapping without structural priors. By integrating a 3D geometric foundation model for on-demand reconstruction and employing dynamic programming-based sequence matching, geometric verification, and confidence calibration, the method achieves coarse-to-fine alignment of multi-session submaps. Evaluated on the Map-Free benchmark, the system attains an average translational error of 0.62 m, maintains global trajectory errors below 3 m over 15 km of multi-session data, and successfully completes 12 autonomous image-goal navigation tasks.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Scalable and maintainable map representations are fundamental to enabling large-scale visual navigation and facilitating the deployment of robots in real-world environments. While collaborative localization across multi-session mapping enhances efficiency, traditional structure-based methods struggle with high maintenance costs and fail in feature-less environments or under significant viewpoint changes typical of crowd-sourced data. To address this, we propose OPENNAVMAP, a lightweight, structure-free topometric system leveraging 3D geometric foundation models for on-demand reconstruction. Our method unifies dynamic programming-based sequence matching, geometric verification, and confidence-calibrated optimization to robust, coarse-to-fine submap alignment without requiring pre-built 3D models. Evaluations on the Map-Free benchmark demonstrate superior accuracy over structure-from-motion and regression baselines, achieving an average translation error of 0.62m. Furthermore, the system maintains global consistency across 15km of multi-session data with an absolute trajectory error below 3m for map merging. Finally, we validate practical utility through 12 successful autonomous image-goal navigation tasks on simulated and physical robots. Code and datasets will be publicly available in https://rpl-cs-ucl.github.io/OpenNavMap_page.

Problem

• structure-free mapping
• collaborative localization
• large-scale navigation
• feature-less environments
• viewpoint changes

Innovation

• structure-free mapping
• topometric mapping
• collaborative localization
• 3D geometric foundation models
• submap alignment