Skip to main content

Advertisement

Log in

A seamless ground truth detection for enhancing localization on mobile robots

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Robot localization mechanism is an essential feature to determine the position of the corresponding robot within an environment, particularly in the field of Standard Platform League (SPL) at the RoboCup. Despite the available input from the onboard sensors, the ground truth information is necessary for a real-time localization system. This study proposes an efficient color-based segmentation scheme using an overhead projective camera with an autonomous calibration procedure. This enhances the system robustness against lighting changes and different labeling setups for the field environment. The experimental results show that the proposed method localizes and recognizes objects with a detection rate of 96.4%.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Benezeth Y, Jodoin PM, Emile B, Laurent H, Rosenberger C (2008) Review and evaluation of commoly-implemented background subtraction algorithms. 19th Int Conf Pattern Recogn 1–4

  2. Bloisi D, Iocchi L (2012) Independent multimodal background subtraction. Int Conf Computational Model Obj Image: Fundamentals 39–44

  3. Calderara S, Melli R, Prati A, Cucchiara R (2006) Reliable background suppression for complex scenes. Proc. 4th ACM Int. Workshop Video Survei. Sens Netw 211–214

  4. Candemir S, Jaeger S, Palanippan K, Musco JP, Singh RK, Xue Z, Karagyris A, Antani S, Thoma G, McDonald CJ (2014) Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging 33(2):577–590

    Article  Google Scholar 

  5. Carminati L, Benois-Pineau J (2005) Gaussian mixture classification for moving object detection in video surveillance environment. IEEE Int Conf Image Process:113–116

  6. Chang C-H, Wang S-C, Wang C-C (2016) Exploiting moving objects: multi-robot simultaneous localization and tracking. IEEE Trans Autom Sci Eng 13(2):810–827

    Article  Google Scholar 

  7. El Baf F, Bouwmans T, Vachon B (2008) Fuzzy integral for moving object detection. IEEE Int Conf Fuzzy Syst 1729–1736

  8. Fox D, Burgard W, Thrun S (1999) Markov localization for mobile robots in dynamic environments. J Artif Intell Res 11:391–427

    Article  MATH  Google Scholar 

  9. Hwang C-L, Chou Y-J, Lan C-W (2013) Comparisons between two visual navigation strategies for kicking to virtual target point of humanoid robots. IEEE Trans Instrum Meas 62(11):3050–3063

    Article  Google Scholar 

  10. Khandelwal P, Stone P (2011) A low cost ground truth detection system using the Kinect. RoboCup 2011: Robot Soccer World Cup XV 7416:515–527

  11. Lee B-J, Stonier D, Kim Y-D, Yoo J-K, Kim J-H (2008) Modifiable walking pattern of a pattern of a humanoid robot by using allowable ZMP variation. IEEE Trans Robot 24(4):917–925

    Article  Google Scholar 

  12. Li X, Lu H, Xiong D, Zhang H, Zheng Z (2013) A survey on visual perception for RoboCup MSL soccer robots. Int J Adv Robot Syst 10:1–10

    Article  Google Scholar 

  13. Lin C-H, Song K-T (2014) Probability-based location aware design and on-demand robotic intrusion detection system. IEEE Trans Syst Man Cybern Syst Hum 44(6):705–715

    Article  Google Scholar 

  14. Manzanera A, Richefeu (2007) A new motion detection algorithm based on Σ-Δ background estimation. Pattern Recogn Lett 28(3):320–328

    Article  Google Scholar 

  15. McCarthy JD, Sasse MA, Miras D (2004) Sharp or smooth? Comparing the effects of quantization vs frame rate for streamed video. Conf Hum Factors Comput Syst 535–542

  16. Minaeian S, Liu J, Son Y-J (2016) Vision-based target detection and localization via a team of cooperative. IEEE Trans Syst Man Cybern Syst Hum 46(7):1005–1016

    Article  Google Scholar 

  17. Nassour J, Hugel V, Ouezdou FB, Cheng G (2013) Qualitative adaptive reward learning with success failure maps: applied to humanoid robot walking. IEEE Trans Neural Networks Learning Syst 24(1):81–93

    Article  Google Scholar 

  18. Niemuller T, Ferrein A, Eckel G, Pirro D, Podbregar P, Kellner T, Rath C, Steinbauer G (2010) Providing ground-truth data for the Nao robot platform. RoboCup 2010: Robot Soccer World Cup XIV 6556:133–144

  19. Pennisi A, Bloisi DD, Iocchi L, Nardi D (2013) Ground truth acquisition of humanoid soccer robot behavior. RoboCup 2013: Robot Soccer World Cup XVII 8371:560–567

  20. RoboCup Technical Committee (2017) RoboCup standard platform league (NAO) rule book. RoboCup 1–10

  21. Roumeliotis S, Bekey G (2002) Distributed multirobot localization. IEEE Trans Robot Autom 18(5):781–795

    Article  Google Scholar 

  22. Sharma G, Wu W, Dalal EN (2005) The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30

    Article  Google Scholar 

  23. Stasse O, Verrelst B, Vanderborght B (2009) Strategies for humanoid robots to dynamically walk over large obstacles. IEEE Trans Robot 25(4):960–967

    Article  Google Scholar 

  24. St-Charles P-L, Bilodeau G-A (2014) Improving background subtraction using local binary similarity patterns. IEEE Winter Conf. Applicat. Computer Vision, Steamboat Springs 509–515

  25. Yoshida Y, Takeuchi K, Miyamoto Y, Sato D, Nenchev D (2013) Postural balance strategies in response to disturbances in the frontal plane and their implementation with a humanoid robot. IEEE Trans Syst Man Cybern Syst Hum 44(6):692–704

    Article  Google Scholar 

  26. Zickler S., Laue T., Birbach O., Wongpati M., Veloso M. (2009) SSL vision: the shared vision system for the RoboCup small size league. RoboCup 2009: Robot Soccer World Cup XIII 5949:425–436

  27. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. 17th Int. Conf. Pattern Recogn 28–31

Download references

Acknowledgements

The authors would like to thank Professor Christopher Whiteley from the National Taiwan University of Science and Technology for proof-reading this manuscript.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chondro, P., Schwarz, I. & Ruan, SJ. A seamless ground truth detection for enhancing localization on mobile robots. Multimed Tools Appl 77, 23149–23166 (2018). https://doi.org/10.1007/s11042-018-5607-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5607-3

Keywords

Navigation