Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 18, pp 23149–23166 | Cite as

A seamless ground truth detection for enhancing localization on mobile robots

  • Peter Chondro
  • Ingmar Schwarz
  • Shanq-Jang Ruan
Article
  • 163 Downloads

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%.

Keywords

Ground truth Object localization Region segmentation Color recognition Mobile robots 

Notes

Acknowledgements

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

References

  1. 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 1–4Google Scholar
  2. 2.
    Bloisi D, Iocchi L (2012) Independent multimodal background subtraction. Int Conf Computational Model Obj Image: Fundamentals 39–44Google Scholar
  3. 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 211–214Google Scholar
  4. 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):577–590CrossRefGoogle Scholar
  5. 5.
    Carminati L, Benois-Pineau J (2005) Gaussian mixture classification for moving object detection in video surveillance environment. IEEE Int Conf Image Process:113–116Google Scholar
  6. 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):810–827CrossRefGoogle Scholar
  7. 7.
    El Baf F, Bouwmans T, Vachon B (2008) Fuzzy integral for moving object detection. IEEE Int Conf Fuzzy Syst 1729–1736Google Scholar
  8. 8.
    Fox D, Burgard W, Thrun S (1999) Markov localization for mobile robots in dynamic environments. J Artif Intell Res 11:391–427CrossRefzbMATHGoogle Scholar
  9. 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):3050–3063CrossRefGoogle Scholar
  10. 10.
    Khandelwal P, Stone P (2011) A low cost ground truth detection system using the Kinect. RoboCup 2011: Robot Soccer World Cup XV 7416:515–527Google Scholar
  11. 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):917–925CrossRefGoogle Scholar
  12. 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 10:1–10CrossRefGoogle Scholar
  13. 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):705–715CrossRefGoogle Scholar
  14. 14.
    Manzanera A, Richefeu (2007) A new motion detection algorithm based on Σ-Δ background estimation. Pattern Recogn Lett 28(3):320–328CrossRefGoogle Scholar
  15. 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 535–542Google Scholar
  16. 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):1005–1016CrossRefGoogle Scholar
  17. 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):81–93CrossRefGoogle Scholar
  18. 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 6556:133–144Google Scholar
  19. 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 8371:560–567Google Scholar
  20. 20.
    RoboCup Technical Committee (2017) RoboCup standard platform league (NAO) rule book. RoboCup 1–10Google Scholar
  21. 21.
    Roumeliotis S, Bekey G (2002) Distributed multirobot localization. IEEE Trans Robot Autom 18(5):781–795CrossRefGoogle Scholar
  22. 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):21–30CrossRefGoogle Scholar
  23. 23.
    Stasse O, Verrelst B, Vanderborght B (2009) Strategies for humanoid robots to dynamically walk over large obstacles. IEEE Trans Robot 25(4):960–967CrossRefGoogle Scholar
  24. 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 509–515Google Scholar
  25. 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):692–704CrossRefGoogle Scholar
  26. 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 5949:425–436Google Scholar
  27. 27.
    Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. 17th Int. Conf. Pattern Recogn 28–31Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic and Computer EngineeringNational Taiwan University of Science and TechnologyTaipeiRepublic of China
  2. 2.Robotics Research InstituteTechnical University of DortmundDortmundFederal Republic of Germany

Personalised recommendations