Skip to main content
SpringerLink
Log in

Error aware multiple vertical planes based visual localization for mobile robots in urban environments

建筑物竖直平面特征误差分析及城市环境中移动机器人视觉定位方法

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

A novel error-aware visual localization method is proposed that utilizes vertical planes, such as vertical building facades in urban areas as landmarks. Vertical planes, reconstructed from coplanar vertical lines, are robust high-level features if compared with point features or line features. Firstly, the error models of vertical lines and vertical planes are built, where maximum likelihood estimation (MLE) is employed to estimate all vertical planes from coplanar vertical lines. Then, the closed-form representation of camera location error variance is derived. Finally, the minimum variance camera pose estimation is formulated into a convex optimization problem, and the weight for each vertical plane is obtained by solving this well-studied problem. Experiments are carried out and the results show that the proposed localization method has an accuracy of about 2 meters, at par with commercial GPS operating in open environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Garro V, Galassi M, Fusiello A. Visual localization for mobile surveillance. In: Proceedings of the IEEE International Conference on Computer Vision, Barcelona, 2011. 80–81

    Google Scholar 

  2. Huang K, Tseng S, Mou W, et al. Simultaneous localization and scene reconstruction with monocular camera. In: Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul, 2012. 2102–2107

    Google Scholar 

  3. Lowe D G. Distinctive image features from scale-invariant keypoints. Intern J Comp Vision, 2004, 60: 91–110

    Article  Google Scholar 

  4. Santosh D, Achar S, Jawahar C. Autonomous image-based exploration for mobile robot navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, 2008. 2717–2722

    Google Scholar 

  5. Cummins M, Newman P. FAB-MAP: Probabilistic localization and mapping in the space of appearance. Intern J Robotics Res, 2008, 27: 647–665

    Article  Google Scholar 

  6. Larlus D, Verbeek J, Jurie F. Category level object segmentation by combining bag-of-words models with dirichlet processes and random fields. Intern J Comp Vision, 2010, 88: 238–253

    Article  MathSciNet  Google Scholar 

  7. Wu L, Hoi S, Yu N. Semantics-preserving bag-of-words models and applications. IEEE Trans Image Proc, 2010, 19: 1908–1920

    Article  MathSciNet  Google Scholar 

  8. Davison A, Reid L, Molton N, et al. Monoslam: Real-time single camera slam. IEEE Trans Pattern Anal Machine Intellig, 2007, 29: 1052–1067

    Article  Google Scholar 

  9. Gil A, Mozos O, Ballesta M, et al. A comparative evaluation of interest point detectors and local descriptors for visual SLAM. Machine Vision Appl, 2010, 21: 905–920

    Article  Google Scholar 

  10. Lemaire T, Lacroix S. Monocular-vision based SLAM using line segments. In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, 2007. 2791–2796

    Google Scholar 

  11. Elqursh A, Elgammal A. Line-based relative pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Colorado Springs, 2011. 3049–3056

    Google Scholar 

  12. Delmerico J, David P, Adelphi M, et al. Building facade detection, segmentation, and parameter estimation for mobile localization and guidance. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, 2011. 1632–1639

    Google Scholar 

  13. Cham T, Ciptadi A, Tan W, et al. Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 366–373

    Google Scholar 

  14. Gee A, Chekhlov D, Calway A, et al. Discovering higher level structure in visual slam. IEEE Trans Robotics, 2008, 24: 980–990

    Article  Google Scholar 

  15. Martinez-Carranza J, Calway A. Unifying planar and point mapping in monocular slam. In: Proceedings of the British Machine Vision Conference, Aberystwyth, 2010. 43.1–43.11

    Google Scholar 

  16. Leung K, Clark C, Huissoon J. Localization in urban environments by matching ground level video images with an aerial image. In: Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, 2008. 551–556

    Google Scholar 

  17. Bansal M, Sawhney H, Cheng H, et al. Geo-localization of street views with aerial image databases. In: Proceedings of the 19th ACM International Conference on Multimedia, Scottsdale, 2011. 1125–1128

    Chapter  Google Scholar 

  18. Selnet T, Elgammal A. Satellite image based precise robot localization on sidewalks. In: Proceedings of the IEEE International Conference on Robotics and Automation. St. Paul, 2012. 2647–2653

    Google Scholar 

  19. Kummerle R, Steder B, Dornhege C, et al. Large scale graph-based SLAM using aerial images as prior information. Auton Robots, 2011, 30: 25–39

    Article  Google Scholar 

  20. Muramatsu S, Tomizawa T, Matsuda H, et al. Mobile robot localization technique using Web-based aerial photos. In: Proceedings of the IEEE International Conference on Mechatronics and Automation, Chengdu, 2012. 1886–1891

    Google Scholar 

  21. Li H, Song D, Lu Y, et al. A two-view based multilayer feature graph for robot navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul, 2012. 3580–3587

    Google Scholar 

  22. Li H, Liu J, Lu X. Visual localization in urban area using orthogonal building boundaries and a GIS database. ROBOT, 2012, 34: 604–613

    Google Scholar 

  23. Hartley R, Zisserman A. Multiple View Geometry in Computer Vision, 2nd Ed. Cambridge: Cambridge University Press, 2004. 30–35

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin, 300300, China

    HaiFeng Li

  2. Institute of Robotics and Automatic Information System, Nankai University, Tianjin, 300071, China

    HongPeng Wang & JingTai Liu

  3. State Key Laboratory of Robotics, Shenyang, 110016, China

    HongPeng Wang

Authors
  1. HaiFeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HongPeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. JingTai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to HaiFeng Li or HongPeng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Wang, H. & Liu, J. Error aware multiple vertical planes based visual localization for mobile robots in urban environments. Sci. China Inf. Sci. 58, 1–14 (2015). https://doi.org/10.1007/s11432-014-5229-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-014-5229-y

Keywords

关键词

Search

Navigation