Intelligent Robot Lab
Robotics Course, School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui, Japan
3-9-1 Bunkyo, Fukui, Fukui, Japan
Our primary research goal is to develop novel and effective methods to enable effective robotic mapping and localization, allowing autonomous mobile robots to accomplish safe and efficient navigation. Our research interests include robot vision, pattern recognition, machine learning, scene understanding, visual tracking, filtering & optimization, database & information retrieval, data compression & recovery, anomaly detection, embodied AI, active vision, coverage path-planning, fault diagnosis, knowledge transfer, multi-modal analysis, and language models.
Our recent publications can be found at the links below.
Map Matching
Since 1995, we are particularly interested in map matching, the ability to match a local map built by a mobile robot to previously built maps, which is crucial in many robotic mapping, self-localization, and simultaneous localization and mapping (SLAM) applications. The goal of map matching is given a pair of input maps, to find a transformation (e.g., rotation, translation) from one map to the other.
One of the most popular solutions to the map matching is RANSAC, in which a number of hypotheses of the transformation are generated from a minimal set of map features while each hypothesis is verified using the remaining features as a cue. Other solutions include information reduction with dimension reduction techniques.
The map matching task is particularly important in the following contexts: