4 accepted papers
Visual Place Recognition (VPR) aims to match query images against a database using visual cues. State-of-the-art methods aggregate features from deep backbones to form global descriptors. Optimal transport-based aggregation methods reformulate feature-to-cluster assignment as a transport problem, bu
Language-guided localization within 3D environments continues to pose a significant challenge for autonomous systems, primarily due to the need for precise alignment between sparse point cloud data and inherently ambiguous natural language descriptions. To address this, we present a novel vision-lan
Mobile robots necessitate advanced natural language understanding capabilities to accurately identify locations and perform tasks such as package delivery. However, traditional visual place recognition (VPR) methods rely solely on single-view visual information and cannot interpret human language de
Vision-Language Place Recognition (VLPR) enhances robot localization performance by incorporating natural language descriptions from images. By utilizing language information, VLPR directs robot place matching, overcoming the constraint of solely depending on vision. However, general multimodal info