Evaluation of different SLAM algorithms using Google tangle data

Liyang Liu, Youbing Wang, Liang Zhao, Shoudong Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publication2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)
PublisherInstitute of Electrical and Electronics Engineers
Pages1954-1959
Number of pages6
ISBN (Electronic)9781538621035
DOIs
Publication statusPublished - 8 Feb 2018
Event12th IEEE Conference on Industrial Electronics and Applications - Siem Reap, Cambodia
Duration: 18 Jun 201720 Jun 2017

Publication series

NameProceedings of the IEEE Conference on Industrial Electronics and Applications
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Electronic)2158-2297

Conference

Conference12th IEEE Conference on Industrial Electronics and Applications
Abbreviated titleICIEA 2017
Country/TerritoryCambodia
CitySiem Reap
Period18/06/1720/06/17

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