Open AccessMachine‐learning‐based system for multi‐sensor 3D localisation of stationary objects
Author(s) -
Berz Everton L.,
Tesch Deivid A.,
Hessel Fabiano P.
Publication year - 2018
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2017.0067
Subject(s) - computer science , support vector machine , artificial intelligence , radio frequency identification , artificial neural network , identification (biology) , range (aeronautics) , real time computing , computer vision , machine learning , pattern recognition (psychology) , engineering , botany , computer security , biology , aerospace engineering
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.