RT Journal Article
A1 Everton L. Berz
A1 Deivid A. Tesch
A1 Fabiano P. Hessel

PB iet
T1 Machine-learning-based system for multi-sensor 3D localisation of stationary objects
JN IET Cyber-Physical Systems: Theory & Applications
VO 3
IS 2
SP 81
OP 88
AB Localisation of objects and people in indoor environments has been widely studied due to security issues and because of the benefits that a localisation system can provide. Indoor positioning systems (IPSs) based on more than one technology can improve localisation performance by leveraging the advantages of distinct technologies. This study proposes a multi-sensor IPS able to estimate the three-dimensional (3D) location of stationary objects using off-the-shelf equipment. By using radio-frequency identification (RFID) technology, machine-learning models based on support vector regression (SVR) and artificial neural networks (ANNs) are proposed. A k-means technique is also applied to improve accuracy. A computer vision (CV) subsystem detects visual markers in the scenario to enhance RFID localisation. To combine the RFID and CV subsystems, a fusion method based on the region of interest is proposed. We have implemented the authors’ system and evaluated it using real experiments. On bi-dimensional scenarios, localisation error is between 9 and 29 cm in the range of 1 and 2.2 m. In a machine-learning approach comparison, ANN performed 31% better than SVR approach. Regarding 3D scenarios, localisation errors in dense environments are 80.7 and 73.7 cm for ANN and SVR models, respectively.
K1 indoor environments
K1 ANN models
K1 artificial neural networks
K1 visual markers
K1 region of interest
K1 Indoor positioning systems
K1 RFID localisation
K1 SVR models
K1 multisensor 3D localisation
K1 bi-dimensional scenarios
K1 k-means technique
K1 stationary objects
K1 CV subsystems
K1 multisensor IPS
K1 people localisation
K1 three-dimensional location
K1 security issues
K1 localisation performance
K1 object localisation
K1 radio-frequency identification technology
K1 machine-learning-based system
K1 support vector regression
K1 computer vision subsystem
K1 localisation error
DO https://doi.org/10.1049/iet-cps.2017.0067
UL https://digital-library.theiet.org/;jsessionid=7rtitdk6mvqut.x-iet-live-01content/journals/10.1049/iet-cps.2017.0067
LA English
SN
YR 2018
OL EN