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Dense Frame-to-Model SLAM with an RGB-D Camera

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

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Abstract

In this paper, a dense frame-to-model Simultaneous Localization And Mapping (SLAM) with an RGB-D camera is proposed, which achieves a more accurate trajectory in contrast to traditional frame-to-model methods. In the frontend, dense photometric information and geometric information are combined to perform a more robust tracking. In the backend, we add volume to loop closure detection to reject false loop. A novel volume-camera pose graph is proposed to effectively reduce drift. Experimental results on some RGB-D SLAM datasets show a reduction of global trajectory error by 18.60% in comparison to Kinituous, 84.43% in comparison to Kinfu.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (Grant No. 61401390).

Author information

Authors and Affiliations

  1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Xiaodan Ye, Jianing Li, Lianghao Wang, Dongxiao Li & Ming Zhang

  2. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, China

    Xiaodan Ye, Jianing Li, Lianghao Wang, Dongxiao Li & Ming Zhang

  3. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Lianghao Wang

Authors
  1. Xiaodan Ye
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  2. Jianing Li
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  3. Lianghao Wang
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  4. Dongxiao Li
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  5. Ming Zhang
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Corresponding authors

Correspondence to Xiaodan Ye or Lianghao Wang .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Ye, X., Li, J., Wang, L., Li, D., Zhang, M. (2018). Dense Frame-to-Model SLAM with an RGB-D Camera. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_56

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

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