A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

Jadidi, MG; Patel, M; Miro, JV; Dissanayake, G; Biehl, J; Girgensohn, A

A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

Jadidi, MG Patel, M Miro, JV

Dissanayake, G

Biehl, J Girgensohn, A

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Publication Type:: Conference Proceeding
Citation:: IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation, 2018
Issue Date:: 2018-11-13

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Field	Value	Language
dc.contributor.author	Jadidi, MG	en_US
dc.contributor.author	Patel, M	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Biehl, J	en_US
dc.contributor.author	Girgensohn, A	en_US
dc.date.available	2020-11-14T18:05:09Z
dc.date.issued	2018-11-13	en_US
dc.identifier.citation	IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation, 2018	en_US
dc.identifier.isbn	9781538656358	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129852
dc.description.abstract	© 2018 IEEE. In this paper, we develop a system for the low-cost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time-varying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.	en_US
dc.relation.ispartof	IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation	en_US
dc.relation.isbasedon	10.1109/IPIN.2018.8533827	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. In this paper, we develop a system for the low-cost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time-varying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.

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http://hdl.handle.net/10453/129852