用文字和地图生成场景图，实现高效户外定位。
GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap
- 用文本描述与地图构建紧凑场景图替代点云
- 城市级测试中处理仅需几秒，存储量大幅降低
- 无需自建地图，直接使用公开的OpenStreetMap数据
我们提出GOTPR，一种适用于户外无GPS环境的鲁棒定位方法。不同于依赖庞大点云地图的现有方法，GOTPR采用从文本描述和地图生成的场景图进行定位，以紧凑的数据结构提升可扩展性，使机器人能高效存储和使用大规模地图数据。同时，该方法无需自建地图，直接利用公开的OpenStreetMap获取全局空间信息。我们在包含对应OpenStreetMap数据的KITTI360Pose数据集上评估性能，结果表明，GOTPR在保持相当精度的同时显著降低存储需求。在城市尺度测试中，处理时间仅需数秒，具备实际机器人应用潜力。
原文摘要 · Abstract (English)
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/.
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