Abstract
Small autonomous florr cleaning robots are the first robots to have entered our homes. These automatic vacuum cleaners have only used ver low-level dirt detection sensors and the vision systems have been constrained to plain-colored and simple-textured floors. However, for industrial applications, where efficiency and the quality of work are paramount, explicit high-level dirt detection is essential. To extend the usability of floor cleaning robots to theses real-world applications, we introduce a more general approach that detects dirt spots on single-colored as well as regularly-textured floors. Dirt detection is approached as a single-class classification problem, using unsupervised online learning of a Gaussian Mixture Model representing the floor pattern. An extensive evaluation shows that our method detects dirt spots on different floor types and that it outperforms state-of-the-art approaches especially for complex floor textures.
This work is supported by the European Commission through the Horizon 2020 Programme (H2020-ICT-2014-1, Grant agreement no: 645376).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Bormann, R., Weisshardt, F., Arbeiter, G., Fischer, J.: Autonomous dirt detection for cleaning in office environments. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1260–1267 (2013)
Bormann, R., Fischer, J., Arbeiter, G., Weisshardt, F., Verl, A.: A visual dirt detection system for mobile service robots. In: 7th German Conference on Robotics, Proceedings of ROBOTIK 2012, pp. 1–6 (2012)
Duda, R.O., Hart, P.E., Stork, D.G., et al.: Pattern Classification, vol. 2. Wiley, New York (1973)
Ren, J., Vlachos, T.: Detection and recovery of film dirt for archive restoration applications. In: 2007 IEEE International Conference on Image Processing, vol. 4, p. 21. IEEE (2007)
Wechtitsch, S., Schallauer, P.: Robust detection of single-frame defects in archived film. In: International Conference on Pattern Recognition (ICPR), pp. 2647–2650 (2012)
Bischof, H., Grabner, H., Rot, P.M., Grabner, M.: Autonomous learning of a robust background model for change detection. In: Ferryman, J.M. (ed.) IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006), pp. 39–46. IEEE (2006)
Potapova, E., Zillich, M., Vincze, M.: Attention-driven segmentation of cluttered 3D scenes. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3610–3613 (2012)
Potapova, E., Zillich, M., Vincze, M.: Local 3D symmetry for visual saliency in 2.5D point clouds. In: Asian Conference on Computer Vision, pp. 434–445 (2013)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Bormann, R., Hampp, J., Hägele, M.: New brooms sweep clean - an autonomous robotic cleaning assistant for professional office cleaning. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4470–4477 (2015)
Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99, 215–249 (2014)
Drews, P., Núñez, P., Rocha, R.P., Campos, M., Dias, J.: Novelty detection and segmentation based on Gaussian mixture models: a case study in 3D robotic laser mapping. Robot. Auton. Syst. 61, 1696–1709 (2013)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Englewood Cliffs (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Grünauer, A., Halmetschlager-Funek, G., Prankl, J., Vincze, M. (2017). The Power of GMMs: Unsupervised Dirt Spot Detection for Industrial Floor Cleaning Robots. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science(), vol 10454. Springer, Cham. https://doi.org/10.1007/978-3-319-64107-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-64107-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64106-5
Online ISBN: 978-3-319-64107-2
eBook Packages: Computer ScienceComputer Science (R0)