Portable Multi-Hypothesis Monte Carlo Localization for Mobile Robots
Alberto García, Francisco Martín, José Miguel Guerrero, Francisco J. Rodríguez, Vicente Matellán
Abstract
Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new localization algorithm that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the system's output. As novelties, our work includes a multi-scale map-matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state of the art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive AMCL solution, obtaining good accuracy/recovery times.
BibTeX
@inproceedings{icra2023_portablemultihyp,
title = {Portable Multi-Hypothesis Monte Carlo Localization for Mobile Robots},
author = {Alberto García and Francisco Martín and José Miguel Guerrero and Francisco J. Rodríguez and Vicente Matellán},
booktitle = {ICRA 2023},
year = {2023}
}