# GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap

arXiv: https://arxiv.org/abs/2501.08575 · HTML: https://mlsanity.com/paper/2501.08575/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions](https://mlsanity.com/paper/2004.03967/) (2020)
- [3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment](https://mlsanity.com/paper/2308.04352/) (2023)
- [SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition](https://mlsanity.com/paper/2011.12430/) (2020)
- [Text2Pos: Text-to-Point-Cloud Cross-Modal Localization](https://mlsanity.com/paper/2203.15125/) (2022)
- [Text2Loc: 3D Point Cloud Localization from Natural Language](https://mlsanity.com/paper/2311.15977/) (2023)
- [SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2404.00469/) (2024)
- [Text to Point Cloud Localization with Relation-Enhanced Transformer](https://mlsanity.com/paper/2301.05372/) (2023)
- ["Where am I?" Scene Retrieval with Language](https://mlsanity.com/paper/2404.14565/) (2024)
- [Towards Traffic Scene Description: The Semantic Scene Graph](https://mlsanity.com/paper/2111.10196/) (2021)
- [MambaPlace:Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms](https://mlsanity.com/paper/2408.15740/) (2024)
- [Planning with Verbal Communication for Human-Robot Collaboration](https://mlsanity.com/paper/1706.04694/) (2017)
- [E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models](https://mlsanity.com/paper/2409.10027/) (2024)
- [Context-Based Visual-Language Place Recognition](https://mlsanity.com/paper/2410.19341/) (2024)

## Built on by (most cited first, 5 of 5)

- [SpatiaLoc: Leveraging Multi-Level Spatial Enhanced Descriptors for Cross-Modal Localization](https://mlsanity.com/paper/2601.03579/) (2026)
- [Riemannian and Symplectic Geometry for Hierarchical Text-Driven Place Recognition](https://mlsanity.com/paper/2604.01598/) (2026)
- [Text2Graph VPR: A Text-to-Graph Expert System for Explainable Place Recognition in Changing Environments](https://mlsanity.com/paper/2512.18613/) (2025)
- [SMc2f: Robust Scenario Mining for Robotic Autonomy from Coarse to Fine](https://mlsanity.com/paper/2601.12010/) (2026)
- [TOL: Textual Localization with OpenStreetMap](https://mlsanity.com/paper/2604.01644/) (2026)

## Abstract (arXiv)

We propose GOTPR, a robust place recognition method designed for outdoor environments where GPS signals are unavailable. Unlike existing approaches that use point cloud maps, which are large and difficult to store, GOTPR leverages scene graphs generated from text descriptions and maps for place recognition. This method improves scalability by replacing point clouds with compact data structures, allowing robots to efficiently store and utilize extensive map data. In addition, GOTPR eliminates the need for custom map creation by using publicly available OpenStreetMap data, which provides global spatial information. We evaluated its performance using the KITTI360Pose dataset with corresponding OpenStreetMap data, comparing it to existing point cloud-based place recognition methods. The results show that GOTPR achieves comparable accuracy while significantly reducing storage requirements. In city-scale tests, it completed processing within a few seconds, making it highly practical for real-world robotics applications. More information can be found at https://donghwijung.github.io/GOTPR_page/.