Summary
This paper presents a method for automatic terrain classification, using a cheap monocular camera in conjunction with a robot’s stall sensor. A first step is to have the robot generate a training set of labelled images. Several techniques are then evaluated for preprocessing the images, reducing their dimensionality, and building a classifier. Finally, the classifier is implemented and used online by an indoor robot. Results are presented, demonstrating an increased level of autonomy.
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References
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. Neural Networks 5(4), 537–550 (1994)
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. Computational Learing Theory, 144–152 (1992)
Cover, T., Thomas, A.: Elements of Information Theory. John Wiley & Sons, Chichester (1991)
Damper, R., Gunn, S.: Learning phonetic distinctions from speech signals. In: Proc. Eurospeech 1999, pp. 2675–2678 (1999)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, Chichester (2001)
Guyon, I., Ellisseef, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Horswill, I.: Polly: A vision-based artificial agent. In: Proceedings of the 11th National Conference on Artificial Intelligence, pp. 824–829 (1993)
Kosecka, J., Zhou, L., Barber, P., Duric, Z.: Qualitative image-based localization in indoors environments. In: Proc. Intl. Conf. on Computer Vision and Pattern Recognition, vol. 2 (2003)
Krose, B., Vlassis, N., Bunschoten, R., Motomura, Y.: A probabilistic model for appearance-based robot localization. Image and Vision Computing 19(6), 381–391 (2001)
Lacroix, S., Mallet, A., Bonnafous, D., Bauzil, G., Fleury, S., Herrb, M., Chatila, R.: Autonomous rover navigation on unknown terrains. International Journal of Robotics Research 21(10–11), 917–942 (2002)
Lorigo, L., Brooks, R., Grimson, W.: Visually-guided obstacle avoidance in unstructured environments. In: Proc. IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pp. 373–379 (1997)
Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and terrain classification for autonomous off-road navigation. Autonomous Robots 18, 81–102 (2005)
Mitchell, T., Labrosse, F.: Visual homing: a purely appearance-based approach. In: Proc. Towards Autonomous Robotic Systems (2004)
Simoncelli, E., Freeman, W.: The steerable pyramid: A flexible architecture for multi-scale derivative computation. In: Proc. Intl. Conf. on Image Processing (1995)
Thrun, S.: Bayesian landmark learning for mobile robot localization. Machine Learning 33(1), 41–76 (1998)
Torkkola, K.: Feature extraction by non-parametric mutual information maximization. Journal of Machine Learning Research 3, 1415–1438 (2003)
Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: International Conference on Computer Vision (2003)
Ulrich, I., Borenstein, J.: Vfh+: Reliable obstacle avoidance for fast mobile robots. In: IEEE International Conference on Robotics and Automation(ICRA 1998), pp. 1572–1577 (1998)
Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: Proceedings of AAAI 2000, pp. 866–871 (2000)
Vlassis, N., Bunschoten, R., Kröse, B.: Learning task-relevant features from robot data. In: Proc. IEEE Int. Conf. on Robotics and Automation, Seoul, Korea (2001)
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Brooks, A., Makarenko, A., Upcroft, B., Durrant-Whyte, H. (2008). Learning Informative Features for Indoor Traversability. In: Khatib, O., Kumar, V., Rus, D. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77457-0_29
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DOI: https://doi.org/10.1007/978-3-540-77457-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77456-3
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