GOTLoc: General Outdoor Text-based Localization Using Scene Graph Retrieval with OpenStreetMap

• URL: http://arxiv.org/abs/2501.08575v1
• Date: Wed, 15 Jan 2025 04:51:10 GMT
• Title: GOTLoc: General Outdoor Text-based Localization Using Scene Graph Retrieval with OpenStreetMap
• Authors: Donghwi Jung, Keonwoo Kim, Seong-Woo Kim,
• Abstract summary: We propose GOTLoc, a robust localization method capable of operating even in outdoor environments where GPS signals are unavailable. The method achieves this robust localization by leveraging comparisons between scene graphs generated from text descriptions and maps. Our results demonstrate that the proposed method achieves accuracy comparable to algorithms relying on point cloud maps.
• Score: 4.51019574688293
• License:
• Abstract: We propose GOTLoc, a robust localization method capable of operating even in outdoor environments where GPS signals are unavailable. The method achieves this robust localization by leveraging comparisons between scene graphs generated from text descriptions and maps. Existing text-based localization studies typically represent maps as point clouds and identify the most similar scenes by comparing embeddings of text and point cloud data. However, point cloud maps have limited scalability as it is impractical to pre-generate maps for all outdoor spaces. Furthermore, their large data size makes it challenging to store and utilize them directly on actual robots. To address these issues, GOTLoc leverages compact data structures, such as scene graphs, to store spatial information, enabling individual robots to carry and utilize large amounts of map data. Additionally, by utilizing publicly available map data, such as OpenStreetMap, which provides global information on outdoor spaces, we eliminate the need for additional effort to create custom map data. For performance evaluation, we utilized the KITTI360Pose dataset in conjunction with corresponding OpenStreetMap data to compare the proposed method with existing approaches. Our results demonstrate that the proposed method achieves accuracy comparable to algorithms relying on point cloud maps. Moreover, in city-scale tests, GOTLoc required significantly less storage compared to point cloud-based methods and completed overall processing within a few seconds, validating its applicability to real-world robotics. Our code is available at https://github.com/donghwijung/GOTLoc.

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