Skip to main content
SpringerLink
Log in
Book cover

Computer Vision in Control Systems-4 pp 71–103Cite as

Unscented RGB-D SLAM in Indoor Environment

  • Chapter
  • First Online:

  • 879 Accesses

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 136))

Abstract

The research considers the implementation of simultaneous localization and mapping algorithm based on the FastSLAM technique and specific problems that are typical for the RGB-D sensor-based solutions. An improvement of the classical FastSLAM algorithm has been obtained by replacing the method of landmarks’ observations filtering with unscented Kalman filters. Instead of linearizing, the nonlinear models through the first order Taylor series expansion at the mean of the landmark state were applied. The proposed algorithm computes a more accurate mean and uncertainty of the landmarks, which are moving nonlinearly. Various data preprocessing issues are discussed, such as the method of calibration of Kinect-like cameras, depth map restoration using a modified interpolation technique, and filtering the noise in the RGB images for more accurate detection of key features. Additionally, the chapter presents an improved resampling algorithm for the particle filtering through the adaptive thresholding based on the data of the effective particle number evolution. The proposed algorithm runs in real time and shows good accuracy and robustness in comparison with other modern SLAM systems using all the advantages and disadvantages of the RGB-D sensors.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Dutta, T.: Evaluation of the Kinect sensor for 3-D kinematic measurement in the workplace. Appl. Ergon. 43(4), 645–649 (2012)

    Article  Google Scholar 

  2. Karlsson, N., di Bernardo E., Ostrowski, J., Goncalves, L., Pirjanian, P., Munich, M.: The vSLAM algorithm for robust localization and mapping. In: International Conference on Robotics and Automation (ICRA’2005), pp. 18–22 (2005)

    Google Scholar 

  3. Aulinas, J.: The SLAM problem: a survey. In: 2008 Conference on Artificial Intelligence Research & Development, pp. 363–371 (2015)

    Google Scholar 

  4. Zakaria, A.M., Said, A.M.: 3D reconstruction of a scene from multiple uncalibrated images using close range photogrammetry. In: International Symposium on Information Technology (ITSim’2010), pp. 1–5 (2010)

    Google Scholar 

  5. Tomasi, C., Kanade, T.: Detection and tracking of point features. Tech Report CMU-CS-91-132, Carnegie-Melon University (1992)

    Google Scholar 

  6. Technical description of Kinect calibration. Available from: http://wiki.ros.org/kinect_calibration/technical. Accessed 3 July 2017

  7. Smisek, J., Jancosek, M., Pajdla, T.: 3D with kinect. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision, Advances in Computer Vision and Pattern Recognition, pp. 3–25. Springer, London (2013)

    Chapter  Google Scholar 

  8. Herrera, C., Kannala, J., Heikkila, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2058–2064 (2012)

    Article  Google Scholar 

  9. Wagner, D., Mulloni, A., Langlotz, T., Schmalstieg, D.: Real-time panoramic mapping and tracking on mobile phones. In: IEEE Virtual Reality Conference (VR’2010), pp. 211–218 (2010)

    Google Scholar 

  10. Einicke, G.A., White, L.B.: Robust extended Kalman filtering. IEEE Trans. Sig. Process. 47(9), 2596–2599 (1999)

    Article  MATH  Google Scholar 

  11. Koller, D., Montemerlo, M., Thrun, S., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: 18th National Conference on Artificial intelligence (AAAI’2002), pp. 593–598 (2002)

    Google Scholar 

  12. Murphy, K.: Bayesian map learning in dynamic environments. In: 12th Int Conf Neural Information Processing Systems (NIPS’1999), pp. 1015–1021 (1999)

    Google Scholar 

  13. Doucet, A., Freitas, N., Murphy, K., Russell, S.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: 16th Conference on Uncertainty in Artificial Intelligence (UAI’2000), pp. 176–183 (2000)

    Google Scholar 

  14. Murphy, K., Russell, S.: Rao-blackwellized particle filtering for dynamic Bayesian networks. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential MonteCarlo Methods in Practice, pp. 499–515. Springer Science+Business Media, New York (2001)

    Chapter  Google Scholar 

  15. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, London (2010)

    MATH  Google Scholar 

  16. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision, 2nd edn. Springer Science+Business Media, London

    Google Scholar 

  17. Barnich, O., Droogenbroeck, M.: A universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  18. Isard, M., Blake, A.: Condensation—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  19. Prozorov, A., Priorov, A.: Three-dimensional reconstruction of a scene with the use of monocular vision. Meas. Tech. 57(10), 1137–1143 (2015)

    Article  Google Scholar 

  20. RGB-D SLAM dataset and benchmark. Available from: https://vision.in.tum.de/data/datasets/rgbd-dataset. Accessed 3 July 2017

  21. PUT Kinect 1 & Kinect 2 data set. Available from: http://lrm.put.poznan.pl/putkk/. Accessed 3 July 2017

  22. The Malaga Stereo and Laser Urban Data Set. Available from: http://www.mrpt.org/MalagaUrbanDataset. Accessed 3 July 2017

  23. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’2012), pp. 573–580 (2012)

    Google Scholar 

  24. Kuemmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., Kleiner, A.: On measuring the accuracy of SLAM algorithms. J Auton. Rob. 27(4), 387–407 (2009)

    Article  Google Scholar 

  25. Oxford Robotical Dataset. Available from: http://robotcar-dataset.robots.ox.ac.uk/. Accessed 3 July 2017

  26. Kurt-Yavuz, Z., Yavuz, S.: A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms. In: IEEE 16th International Conference on Intelligent Engineering Systems (INES’2012), pp. 37–43 (2012)

    Google Scholar 

  27. Wan, E.A., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium (AS-SPCC’2000), pp. 153–158 (2000)

    Google Scholar 

  28. Simon, J., Jeffrey, U.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)

    Article  Google Scholar 

  29. Wan, E.A., Van der Merwe, R.: The square-root unscented Kalman filter for state and parameter-estimation. In: IEEE International Conference on Acoustics, Speech, and Signal (ICASSP’2001), pp. 3461–3464 (2001)

    Google Scholar 

  30. Camera Calibration Toolbox for Matlab. Available from: http://www.vision.caltech.edu/bouguetj/calib_doc. Accessed 3 July 2017

  31. Wang, H.M., Huang, C.H., Yang, J.F.: Depth maps interpolation from existing pairs of keyframes and depth maps for 3D video generation. In: IEEE International Symposium on Circuits and Systems (ISCAS’2010), pp. 3248–3251 (2010)

    Google Scholar 

  32. Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)

    Article  Google Scholar 

  33. Canny, J.A.: Computational approach to edge detection. IEEE Trans Pattern Anal. Mach. Intell. (PAMI) 8(6), 679–698 (1988)

    Google Scholar 

  34. Blanco, J.L.: Contributions to localization, mapping and navigation in mobile robotics. Ph.D. Thesis, Universidad de Malaga

    Google Scholar 

  35. Douc, R., Cappe, O., Moulines, E.: Comparison of resampling schemes for particle filtering. In: 4th International Symposium on Image and Signal Processing and Analysis (ISPA’2005), pp. 64–69 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. P.G. Demidov Yaroslavl State University, 14 Sovetskaya st., Yaroslavl, 150000, Russian Federation

    Alexander Prozorov, Andrew Priorov & Vladimir Khryashchev

Authors
  1. Alexander Prozorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Priorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladimir Khryashchev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Prozorov .

Editor information

Editors and Affiliations

  1. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Prozorov, A., Priorov, A., Khryashchev, V. (2018). Unscented RGB-D SLAM in Indoor Environment. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems-4. Intelligent Systems Reference Library, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-319-67994-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67994-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67993-8

  • Online ISBN: 978-3-319-67994-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation