TY - GEN
T1 - Evaluation of different SLAM algorithms using Google tangle data
AU - Liu, Liyang
AU - Wang, Youbing
AU - Zhao, Liang
AU - Huang, Shoudong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/8
Y1 - 2018/2/8
N2 - In this paper, we evaluate three state-of-the-art Simultaneous Localization and Mapping (SLAM) methods using data extracted from a state-of-the-art device for indoor navigation - the Google Tango tablet. The SLAM algorithms we investigated include Preintegration Visual Inertial Navigation System (VINS), ParallaxBA and ORB-SLAM. We first describe the detailed process of obtaining synchronized IMU and image data from the Google Tango device, then we present some of the SLAM results obtained using the three different SLAM algorithms, all with the datasets collected from Tango. These SLAM results are compared with that obtained from Tango's inbuilt motion tracking system. The advantages and failure modes of the different SLAM algorithms are analysed and illustrated thereafter. The evaluation results presented in this paper are expected to provide some guidance on further development of more robust SLAM algorithms for robotic applications.
AB - In this paper, we evaluate three state-of-the-art Simultaneous Localization and Mapping (SLAM) methods using data extracted from a state-of-the-art device for indoor navigation - the Google Tango tablet. The SLAM algorithms we investigated include Preintegration Visual Inertial Navigation System (VINS), ParallaxBA and ORB-SLAM. We first describe the detailed process of obtaining synchronized IMU and image data from the Google Tango device, then we present some of the SLAM results obtained using the three different SLAM algorithms, all with the datasets collected from Tango. These SLAM results are compared with that obtained from Tango's inbuilt motion tracking system. The advantages and failure modes of the different SLAM algorithms are analysed and illustrated thereafter. The evaluation results presented in this paper are expected to provide some guidance on further development of more robust SLAM algorithms for robotic applications.
UR - http://www.scopus.com/inward/record.url?scp=85047465575&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2017.8283158
DO - 10.1109/ICIEA.2017.8283158
M3 - Conference contribution
AN - SCOPUS:85047465575
T3 - Proceedings of the IEEE Conference on Industrial Electronics and Applications
SP - 1954
EP - 1959
BT - 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)
PB - Institute of Electrical and Electronics Engineers
T2 - 12th IEEE Conference on Industrial Electronics and Applications
Y2 - 18 June 2017 through 20 June 2017
ER -