Search Results for author:
Found 5 papers, 1 papers with code
CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps
This paper describes a multi-modal data association method for global localization using object-based maps and camera images.
Single-Shot Global Localization via Graph-Theoretic Correspondence Matching
This paper describes a method of global localization based on graph-theoretic association of instances between a query and the prior map.
Multi-Source Soft Pseudo-Label Learning with Domain Similarity-based Weighting for Semantic Segmentation
This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset.
Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments
This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving.
Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots
The core of our idea is to use multiple rich image datasets of different environments with segmentation labels to generate pseudo-labels for the target images to effectively transfer the knowledge from multiple sources and realize a precise training of semantic segmentation.
