iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM System

Wei, Bo, Trigoni, Niki and Markham, Andrew (2022) iMag+: An Accurate and Rapidly Deployable Inertial Magneto-Inductive SLAM System. IEEE Transactions on Mobile Computing, 21 (10). pp. 3644-3655. ISSN 1536-1233

TMC_final_iMag_Accurate_and_Rapidly_Deployable_Inertial_Magneto_Inductive_Localisation.pdf - Accepted Version

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Localisation is an important part of many applications. Our motivating scenarios are short-term construction work and emergency rescue. These scenarios also require rapid setup and robustness to environmental conditions additional to localisation accuracy. These requirements preclude the use of many traditional high-performance methods, e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To overcome these challenges, we introduce iMag+, an accurate and rapidly deployable inertial magneto-inductive (MI) mapping and localisation system, which only requires monitored workers to carry a single MI transmitter and an inertial measurement unit in order to localise themselves with minimal setup effort. However, one major challenge is to use distorted and ambiguous MI location estimates for localisation. To solve this challenge, we propose a novel method to use MI devices for sensing environmental distortions for accurate closing inertial loops. We also suggest a robust and efficient first quadrant estimator to sanitise the ambiguous MI estimates. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy and can improve performance significantly compared with only using a Magneto-inductive device or inertial measurement unit (IMU) for localisation.

Item Type: Article
Uncontrolled Keywords: Magneto-inductive device, Inertial measurements, Localisation, SLAM
Subjects: G400 Computer Science
H200 Civil Engineering
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 09 Mar 2021 09:40
Last Modified: 26 Sep 2022 13:45

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