We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filte...
— Self-localization is a major research task in mobile robotics for several years. Efficient self-localization methods have been developed, among which probabilistic Monte-Carlo...
- Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Many studies have been conducted to improve performance of MCL. Al...
In this paper we present a vision-based approach to mobile robot localization, that integrates an image retrieval system with Monte-Carlo localization. The image retrieval process...
Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabi...