Evaluation of different SLAM algorithms using Google tangle data

Publication Type:
Conference Proceeding
Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1954 - 1959
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© 2017 IEEE. 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.
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