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

• URL: http://arxiv.org/abs/2601.12291v1
• Date: Sun, 18 Jan 2026 07:24:46 GMT
• Title: OpenNavMap: Structure-Free Topometric Mapping via Large-Scale Collaborative Localization
• Authors: Jianhao Jiao, Changkun Liu, Jingwen Yu, Boyi Liu, Qianyi Zhang, Yue Wang, Dimitrios Kanoulas,
• Abstract summary: OpenNavMap is a lightweight, structure-free topometric system leveraging 3D geometric foundation models for on-demand reconstruction.<n>Our method unifies dynamic programming-based sequence matching, geometric verification, and confidence-calibrated optimization to robust, coarse-to-fine submap alignment.<n> Evaluations on the Map-Free benchmark demonstrate superior accuracy over structure-from-motion and regression baselines, achieving an average translation error of 0.62m.
• Score: 12.686154192361913
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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