Paper Info
Title
Effect of Kernel Function to Magnetic Map and Evaluation of Localization of Magnetic Navigation
Abstract
Localization is one of the most fundamental requirements for the use of autonomous robots. In this work, we use magnetic-based localization; which, while not as accurate as laser rangefinder or camera-based systems, is not affected by a large number of people on its surrounding, making it ideal for applications where this is expected, such as service robotics in supermarkets, hotels, etc. Magnetic-based localization systems first create a magnetic map of the environment using magnetic samples acquired a priori. An approach for generating this map is to use collected data to training a Gaussian Process model. Gaussian Processes are non-parametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. The purpose of this study is to improve the accuracy of the magnetic localization by testing several kernel functions and experimentally verifying its effects on robot localization.
Year
DOI
Venue
2020
10.1109/MFI49285.2020.9235259
2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Keywords
DocType
ISBN
autonomous robots,magnetic navigation,robot localization,magnetic localization,adequate kernel function,data-drive models,Gaussian Process model,magnetic samples,magnetic map,magnetic-based localization systems,service robotics,camera-based systems
Conference
978-1-7281-6423-6
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Takumi Takebayashi100.34
Renato Miyagusuku286.92
Koichi Ozaki314152.36