OpenNavMap

Structure-Free Topometric Mapping via Large-Scale Collaborative Localization

---

🚀 Overview

OpenNavMap is a lightweight, structure-free topometric mapping system that enables large-scale collaborative localization across multiple sessions without requiring pre-built 3D models. It leverages 3D geometric foundation models for on-demand reconstruction and provides robust metric localization performance.

---

✨ Key Features

• 🎯 Structure-free Map: Lightweight graph-based map representation
• 🔗 Collaborative Localization: Global registration across sessions in large-scale environments
• 📱 Cross-Device: Works on various mobile platforms
• 🔄 Scalable & Lifelong: Automatic map maintenance
• 🗺️ Multi-Session: Merge maps from different agents/times

---

🛠️ Quick Start

Code is coming soon!

---

🎬 Results Gallery

Dataset

Overview of our self-collected dataset using multiple devices, spanning diverse environments over 3.5 months, 35 sequences, and 18.7km.

Multi-Session Mapping

Multi-session mapping with heterogeneous devices across two regions.

Real-World Image-Goal Navigation

Quadruped robot performing image-goal navigation.

Autonomous navigation across varied outdoor environments with obstacles.

---

📚 Acknowledgement

If this work is helpful to your research, please consider citing OpenNavMap or our related works:

@article{jiao2025opennavmap,
  title={OpenNavMap: Structure-Free Topometric Mapping via Large-Scale Collaborative Localization},
  author={Jiao, Jianhao and Liu, Changkun and Yu, Jingwen and Liu, Boyi and Zhang, Qianyi and Wang, Yue and Kanoulas, Dimitrios},
  journal={Under Review},
  year={2025}
}

@inproceedings{jiao2025litevloc,
  title={LiteVLoc: Map-lite visual localization for image goal navigation},
  author={Jiao, Jianhao and He, Jinhao and Liu, Changkun and Aegidius, Sebastian and Hu, Xiangcheng and Braud, Tristan and Kanoulas, Dimitrios},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={5244--5251},
  year={2025},
  organization={IEEE}
}

@article{wei2025fusionportablev2,
  title={Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments},
  author={Wei, Hexiang and Jiao, Jianhao and Hu, Xiangcheng and Yu, Jingwen and Xie, Xupeng and Wu, Jin and Zhu, Yilong and Liu, Yuxuan and Wang, Lujia and Liu, Ming},
  journal={The International Journal of Robotics Research},
  volume={44},
  number={7},
  pages={1093--1116},
  year={2025},
  publisher={SAGE Publications Sage UK: London, England}
}

---

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

🤝 Contact & Links

• Project Page: https://rpl-cs-ucl.github.io/OpenNavMap_page
• Contact: Jianhao Jiao (jiaojh1994@gmail.com), Prof.Dimitrios Kanoulas (d.kanoulas@ucl.ac.uk)
• Acknowledgments: Supported by UKRI Future Leaders Fellowship [MR/V025333/1] (RoboHike)

---

_{Built with ❤️ by the Robot Perception and Learning Lab at UCL}