Simultaneous Localization and Mapping Technology Based on Project Tango

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Date: Apr. 2019
Publisher: Springer
Document Type: Report
Length: 230 words

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Abstract :

Byline: Pei Xu (1), Kehua Su (1), Cheng Hong (1), Dengyi Zhang (1) Keywords: simultaneous localization and mapping; Project Tango; loop closure detection; visual vocabulary; graph optimization; TP 391.41 Abstract: Aiming at the problem of system error and noise in simultaneous localization and mapping (SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on visual vocabulary with memory management. The graph optimization is also combined to achieve a running application. First, the color image and depth information of the environment are collected to establish the calibration model of system error and noise. Second, with constraint condition provided by loop closure detection algorithm, speed up robust feature is calculated and matched. Finally, the motion pose model is solved, and the optimal scene model is determined by graph optimization method. This method is compared with Open Constructor for reconstruction on several experimental scenarios. The results show the number of model's points and faces are larger than Open Constructor's, and the scanning time is less than Open Constructor's. The experimental results show the feasibility and efficiency of the proposed algorithm. Author Affiliation: (1) 0000 0001 2331 6153, grid.49470.3e, School of Computer, Wuhan University, Wuhan, Hubei, 430072, China Article History: Registration Date: 19/03/2019 Received Date: 20/06/2018 Online Date: 20/03/2019 Article note: Foundation item: Supported by the National Natural Science Foundation of China ( 61772379)

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Gale Document Number: GALE|A579551809