Global and Local Localizations Using an Environmental Magnetic Field and a Scan Matching Method

Global and local localizations are critical for mobile robots to perform tasks without human support. In this study, we proposed a new method for global and local localizations that combines an environmental magnetic field with a scan matching method. In this method, we record the environmental magnetic field at each waypoint during the map-building process. To perform global localization, we rotate the mobile robot’s magnetic sensor-detected heading direction (current magnetic heading direction) to approximate the heading direction of the environmental magnetic field stored on the map. Subsequently, we use the scan matching method at positions around each waypoint. The mobile robot is currently in the position with the highest matching rate. The experimental results show that the mobile robot can successfully localize almost anywhere in the indoor environment, with the exception of a position where the magnetic sensor is affected by electric boards, and has a high success rate in an outdoor environment. For the local localization, we calculate the deviation between the mobile robot’s current magnetic heading direction and the one stored on the map, and then use this deviation to improve the localization using the scan matching method. The experimental results show that the mobile robot can perform much stronger localization during autonomous navigation.

Institutions

Publication Details

Journal
Journal of Robotics and Mechatronics
Published
2025-12-19
DOI
https://doi.org/10.20965/jrm.2025.p1365
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Global and Local Localizations Using an Environmental Magnetic Field and a Scan Matching Method

Journal of Robotics and Mechatronics
Robotics and Sensor-Based Localization
article

Global and Local Localizations Using an Environmental Magnetic Field and a Scan Matching Method

article en

Abstract

Global and local localizations are critical for mobile robots to perform tasks without human support. In this study, we proposed a new method for global and local localizations that combines an environmental magnetic field with a scan matching method. In this method, we record the environmental magnetic field at each waypoint during the map-building process. To perform global localization, we rotate the mobile robot’s magnetic sensor-detected heading direction (current magnetic heading direction) to approximate the heading direction of the environmental magnetic field stored on the map. Subsequently, we use the scan matching method at positions around each waypoint. The mobile robot is currently in the position with the highest matching rate. The experimental results show that the mobile robot can successfully localize almost anywhere in the indoor environment, with the exception of a position where the magnetic sensor is affected by electric boards, and has a high success rate in an outdoor environment. For the local localization, we calculate the deviation between the mobile robot’s current magnetic heading direction and the one stored on the map, and then use this deviation to improve the localization using the scan matching method. The experimental results show that the mobile robot can perform much stronger localization during autonomous navigation.

Journal of Robotics and MechatronicsVol. 37(6)
Utsunomiya University (JP), National Institute of Technology, Oyama College (JP), Saitama University (JP)
Openalex Percentile: Top 25%
Robotics and Sensor-Based Localization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.