2024/02/08 by Shigemichi Matsuzaki, Takuma Sugino, Matsuzaki, Shigemichi +11 · 5 citations
Computer Science · Social Sciences · #Advanced Computational Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Data Management and Algorithms #FOS: Computer and information sciences #Geographic Information Systems Studies #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2402.06092
openalex publication_date 2024/02/08 · openalex created_date 2024/02/13 · openalex updated_date 2026/07/28
This paper describes a multi-modal data association method for global localization using object-based maps and camera images. In global localization, or relocalization, using object-based maps, existing methods typically resort to matching all possible combinations of detected objects and landmarks with the same object category, followed by inlier extraction using RANSAC or brute-force search. This approach becomes infeasible as the number of landmarks increases due to the exponential growth of correspondence candidates. In this paper, we propose labeling landmarks with natural language descriptions and extracting correspondences based on conceptual similarity with image observations using a Vision Language Model (VLM). By leveraging detailed text information, our approach efficiently extracts correspondences compared to methods using only object categories. Through experiments, we demonstrate that the proposed method enables more accurate global localization with fewer iterations compared to baseline methods, exhibiting its efficiency.