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Visual SLAM Location Methods Based on Complex Scenes: A Review

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12240))

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Abstract

In recent years, positioning for simple static scenes has been unable to meet the requirements of people’s production and life. People want to achieve accurate positioning in practical scenarios such as airports, exhibition halls and stations. Therefore, the research on visual SLAM positioning in complex dynamic scenes is increasing day by day. This article reviews the research results of SLAM positioning methods and visual SLAM positioning methods for complex scenes in recent years. Firstly, the development process of laser SLAM, visual SLAM, semantic SLAM and multi-sensor fusion is introduced, but the focus is on visual SLAM. Secondly, the paper summarizes the methods of moving object detection and visual SLAM localization in complex dynamic scenes. Then the paper describes the development of deep learning and multi-sensor fusion in visual SLAM positioning based on complex dynamic scenes. Finally, the shortcomings of visual SLAM positioning methods based on complex scenes are summarized and the research prospects are prospected.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant No. 61640305). This research was financially supported by the project of Thousands outstanding young teachers’ training in higher education institutions of Guangxi, The Young and Middle-aged Teachers Research Fundamental Ability Enhancement of Guangxi University (ID: 2019KY0621), Natural Science Foundation of Guangxi Province (No. 2018GXNSFAA281164). Guangxi Colleges and Universities Key Laboratory Breeding Base of System Control and Information Processing, Hechi University research project start-up funds (XJ2015KQ004), Supported by Colleges and Universities Key Laboratory of Intelligent Integrated Automation (GXZDSY2016-04), Hechi City Science and Technology Project (1694-3-2), Research on multi robot cooperative system based on artificial fish swarm algorithm (2017CFC811).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, 545000, Guangxi, China

    Hanxiao Zhang & Jiansheng Peng

  2. School of Physics and Mechanical and Electronic Engineering, Hechi University, Yizhou, 546300, Guangxi, China

    Jiansheng Peng

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  1. Hanxiao Zhang
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Correspondence to Jiansheng Peng .

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  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zhang, H., Peng, J. (2020). Visual SLAM Location Methods Based on Complex Scenes: A Review. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_43

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  • DOI: https://doi.org/10.1007/978-3-030-57881-7_43

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