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Proceedings Paper

Loose fusion based on SLAM and IMU for indoor environment
Author(s): Haijiang Zhu; Zhicheng Wang; Jinglin Zhou; Xuejing Wang
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Paper Abstract

The simultaneous localization and mapping (SLAM) method based on the RGB-D sensor is widely researched in recent years. However, the accuracy of the RGB-D SLAM relies heavily on correspondence feature points, and the position would be lost in case of scenes with sparse textures. Therefore, plenty of fusion methods using the RGB-D information and inertial measurement unit (IMU) data have investigated to improve the accuracy of SLAM system. However, these fusion methods usually do not take into account the size of matched feature points. The pose estimation calculated by RGB-D information may not be accurate while the number of correct matches is too few. Thus, considering the impact of matches in SLAM system and the problem of missing position in scenes with few textures, a loose fusion method combining RGB-D with IMU is proposed in this paper. In the proposed method, we design a loose fusion strategy based on the RGB-D camera information and IMU data, which is to utilize the IMU data for position estimation when the corresponding point matches are quite few. While there are a lot of matches, the RGB-D information is still used to estimate position. The final pose would be optimized by General Graph Optimization (g2o) framework to reduce error. The experimental results show that the proposed method is better than the RGB-D camera’s method. And this method can continue working stably for indoor environment with sparse textures in the SLAM system.

Paper Details

Date Published: 10 April 2018
PDF: 9 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061545 (10 April 2018); doi: 10.1117/12.2302929
Show Author Affiliations
Haijiang Zhu, Beijing Univ. of Chemical Technology (China)
Zhicheng Wang, Beijing Univ. of Chemical Technology (China)
Jinglin Zhou, Beijing Univ. of Chemical Technology (China)
Xuejing Wang, Beijing Univ. of Chemical Technology (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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