TY - GEN
T1 - IMag
T2 - 2018 IEEE International Conference on Robotics and Automation
AU - Wei, Bo
AU - Trigoni, Niki
AU - Markham, Andrew
PY - 2018/9/13
Y1 - 2018/9/13
N2 - Localisation is of importance for many applications. Our motivating scenarios are short-term construction work and emergency rescue. Not only is accuracy necessary, these scenarios also require rapid setup and robustness to environmental conditions. These requirements preclude the use of many traditional methods e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To solve these challenges, we introduce iMag, an accurate and rapidly deployable inertial magneto-inductive (MI) localisation system. It localises monitored workers using a single MI transmitter and inertial measurement units with minimal setup effort. However, MI location estimates can be distorted and ambiguous. To solve this problem, we suggest a novel method to use MI devices for sensing environmental distortions, and use these to correctly close inertial loops. 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 an inertial measurement unit (IMU) and MI device for localisation.
AB - Localisation is of importance for many applications. Our motivating scenarios are short-term construction work and emergency rescue. Not only is accuracy necessary, these scenarios also require rapid setup and robustness to environmental conditions. These requirements preclude the use of many traditional methods e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To solve these challenges, we introduce iMag, an accurate and rapidly deployable inertial magneto-inductive (MI) localisation system. It localises monitored workers using a single MI transmitter and inertial measurement units with minimal setup effort. However, MI location estimates can be distorted and ambiguous. To solve this problem, we suggest a novel method to use MI devices for sensing environmental distortions, and use these to correctly close inertial loops. 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 an inertial measurement unit (IMU) and MI device for localisation.
UR - http://www.scopus.com/inward/record.url?scp=85063151708&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2018.8460804
DO - 10.1109/ICRA.2018.8460804
M3 - Conference contribution
AN - SCOPUS:85063151708
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 99
EP - 106
BT - 2018 IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 May 2018 through 25 May 2018
ER -