Advertisement

Enabling Robust Localization for Automated Guided Carts in Dynamic Environments

  • Christoph HansenEmail author
  • Kay Fuerstenberg
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

The range of applications for autonomous guided carts (AGC) is increasingly growing. Especially in industrial environments ensuring high safety standards in combination with high availability and flexibility are major requirements. For this reason, knowledge about its own position in the environments becomes particularly important. For AGC with low vehicle height localization approaches based on contour observations are widespread. However, in over-time-changing environments the robustness of these techniques is limited. This paper proposes an approach for updating the underlying map in real time during operation. This map update allows for a long-term robust localization. The proposed approach is evaluated for a dynamic test scenario using a cellular transport vehicle.

Keywords

Map update Dynamic environment Localization Pose estimation Long term Robust Accuracy evaluation Autonomous guided vehicle AGV Autonomous guided cart AGC Industrial applications 

References

  1. Behnke J (2014) Logistische Regressionsanalyse: Eine Einfuehrung, SpringerGoogle Scholar
  2. Beinschob P, Reinke C, (2015) Graph SLAM based mapping for AGV localization in large-scale warehouses. In: IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, pp. 245–248Google Scholar
  3. Dellaert F et al (1999) Monte carlo localization for mobile robots. Robot Autom 2:1322–1328CrossRefGoogle Scholar
  4. Fernandez-Madrigal J A (2012) Simultaneous localization and mapping for mobile robots: introduction and methods, IGI globalGoogle Scholar
  5. Fox D (2003) Adapting the sample size in particle filters through KLD-sampling. Int J Robot Res 22(12):985–1003CrossRefGoogle Scholar
  6. Fujii A et al (2015) Detection of localization failure using logistic regression. In: Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Robotics and Automation. Hamburg, pp. 4313–4318Google Scholar
  7. Gutmann JS et al (1998) An experimental comparison of localization methods. Intell Robot Syst 2:736–743Google Scholar
  8. Gustafsson F et al (2002) Particle filters for positioning, navigation and tracking. In: 11th European Conference on Signal Processing Toulouse, pp. 1–4Google Scholar
  9. Kamagaew A et al (2011) Concept of cellular transport systems in facility logistics. In: 5th International Conference on Automation Robotics and Applications (ICARA). pp. 4045Google Scholar
  10. Kirsch C et al (2002) Comparison of localization algorithms for AGVs in industrial environmentsGoogle Scholar
  11. Kirsch C Roehrig C (2011) Global localization and position tracking of an automated guided vehicle. In: Proceedings of the 18th IFAC World Congress. MailandGoogle Scholar
  12. Kleiner A et al (2011) Armo: adaptive road map optimization for large robot teams. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, pp. 3276–3282Google Scholar
  13. Meyer-Delius D (2011) Probabilistic modeling of dynamic environments for mobile robots. PhD, Albert-Ludwigs-Universitt, Freiburg im BreisgauGoogle Scholar
  14. Mongillo G, Deneve S (2008) Online learning with hidden Markov models. Neural Comput 20(7):1706–1716MathSciNetCrossRefzbMATHGoogle Scholar
  15. Pampel F C (2000) Logistic regression: A primer, vol 132, Sage PublicationsGoogle Scholar
  16. Schulz D et al (2003) People tracking with anonymous and id-sensors using rao-blackwellised particle filters.In: IJCAI. pp. 921–928Google Scholar
  17. Tipaldi GD et al (2013) Lifelong localization in changing environments. Int J Robot Res 32(14):1662–1678CrossRefGoogle Scholar
  18. Valencia R et al (2014) Localization in highly dynamic environments using dual-timescale NDT-MCL. In: IEEE International Conference on Robotics and Automation, pp. 3956–396Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.SICK AGHamburgGermany
  2. 2.SICK AGWaldkirchGermany

Personalised recommendations