CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps
This work addresses the scalability issue in global localization for robotics or autonomous systems by reducing computational complexity, though it is incremental as it builds on existing object-based mapping and VLM techniques.
The paper tackles the problem of global localization in object-based maps by proposing a multi-modal data association method that uses natural language descriptions and a Vision Language Model to match landmarks with image observations, resulting in more accurate localization with fewer iterations compared to baseline methods.
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.