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ImPL-VIO: An Improved Monocular Visual-Inertial Odometry Using Point and Line Features

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Intelligent Robotics and Applications (ICIRA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12595))

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Abstract

Most of the visual-inertial navigation systems (VINS) that use only point features usually work well in regular environment, but decay in low-texture scenes. Meanwhile, those systems rarely construct environmental map with structural information. In this paper, an improved tightly-coupled monocular visual-inertial odometry (ImPL-VIO) is developed. The whole system is composed of point and line feature tracking, inertial measurements processing, pose estimator and loop closure detection. For the better use of monocular line observations in the sliding window based pose estimator, an improved line triangulation algorithm is proposed after a detailed analysis of error sources. In addition, we, for the first time, employ the closest point (CP) representation for spatial lines to optimization-based VINS system, and derive the corresponding Jacobians analytically. Finally, simulation and real-world experiments are conducted to validate the proposed system.

Supported by the Program “Research on Basic and Key Technologies of Intelligent Robots" (No. X190021TB190), Ji Hua Laboratory, Guangdong, China.

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Notes

  1. 1.

    https://github.com/MichaelGrupp/evo.git.

References

  1. Huang, G.: Visual-inertial navigation: a concise review. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9572–9582. IEEE (2019)

    Google Scholar 

  2. Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint kalman filter for vision-aided inertial navigation. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3565–3572. IEEE (2007)

    Google Scholar 

  3. Qin, T., Li, P., Shen, S.: Vins-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34(4), 1004–1020 (2018)

    Article  Google Scholar 

  4. Sibley, G., Matthies, L., Sukhatme, G.: Sliding window filter with application to planetary landing. J. Field Robot. 27, 587–608 (2010)

    Article  Google Scholar 

  5. Zhang, G., Lee, J.H., Lim, J., Suh, I.H.: Building a 3-d line-based map using stereo slam. IEEE Trans. Robot. 31(6), 1364–1377 (2015)

    Article  Google Scholar 

  6. Zhang, G., Suh, I.H.: Building a partial 3D line-based map using a monocular slam. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1497–1502. IEEE (2011)

    Google Scholar 

  7. Bartoli, A., Sturm, P.: Structure-from-motion using lines: representation, triangulation, and bundle adjustment. Comput. Vis. Image Understanding 100(3), 416–441 (2005)

    Article  Google Scholar 

  8. Zuo, X., Xie, X., Liu, Y., Huang, G.: Robust visual slam with point and line features. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1775–1782. IEEE (2017)

    Google Scholar 

  9. He, Y., Zhao, J., Guo, Y., He, W., Yuan, K.: Pl-vio: tightly-coupled monocular visual-inertial odometry using point and line features. Sensors 18(4), 1159 (2018)

    Article  Google Scholar 

  10. Gomez-Ojeda, R., Moreno, F.A., Zuñiga-Noël, D., Scaramuzza, D., Gonzalez-Jimenez, J.: Pl-slam: a stereo slam system through the combination of points and line segments. IEEE Trans. Robot. 35(3), 734–746 (2019)

    Article  Google Scholar 

  11. Zhao, Wei., Qian, Kun., Ma, Zhewen., Ma, Xudong, Yu, Hai: Stereo visual SLAM using bag of point and line word Pairs. In: Yu, Haibin, Liu, Jinguo, Liu, Lianqing, Ju, Zhaojie, Liu, Yuwang, Zhou, Dalin (eds.) ICIRA 2019. LNCS (LNAI), vol. 11743, pp. 651–661. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27538-9_56

    Chapter  Google Scholar 

  12. Yang, Y., Geneva, P., Eckenhoff, K., Huang, G.: Visual-inertial odometry with point and line features. Macau, China, November 2019

    Google Scholar 

  13. Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M.W., Siegwart, R.: The euroc micro aerial vehicle datasets. Int. J. Robot. Res. 35(10), 1157–1163 (2016)

    Article  Google Scholar 

  14. Lucas, B.D., et al.: An iterative image registration technique with an application to stereo vision (1981)

    Google Scholar 

  15. Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2008)

    Article  Google Scholar 

  16. Zhang, L., Koch, R.: An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency. J. Visual Commun. Image Representation 24(7), 794–805 (2013)

    Article  Google Scholar 

  17. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China

    Haoqi Cheng & Hong Wang

  2. Engineering Research Center for Intelligent Robotics, Ji Hua Laboratory, Guangdong, China

    Zhongxue Gan & Jinxiang Deng

Authors
  1. Haoqi Cheng
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  2. Hong Wang
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  3. Zhongxue Gan
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  4. Jinxiang Deng
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Corresponding author

Correspondence to Hong Wang .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  2. Harbin Institute of Technology, Harbin, China

    Hong Liu

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Monash University, Selangor, Malaysia

    Chern Hong Lim

  5. Tsinghua University, Beijing, China

    Xinjun Liu

  6. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  7. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Kam Meng Goh

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Cheng, H., Wang, H., Gan, Z., Deng, J. (2020). ImPL-VIO: An Improved Monocular Visual-Inertial Odometry Using Point and Line Features. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-66645-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66644-6

  • Online ISBN: 978-3-030-66645-3

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