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Autonomous exploration using rapid perception of low-resolution image information

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Abstract

We present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32×24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor, and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage of the pixels both spatially and temporally, processing occurs at an average of 1000 frames per second, thus freeing the processor for other concurrent tasks.

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References

  • Ancona, N., & Poggio, T. (1995). Optical flow from 1-D correlation: Application to a simple time-to-crash detector. International Journal of Computer Vision, 14(2), 131–146.

    Article  Google Scholar 

  • Atallah, M. J. (1985). On symmetry detection. IEEE Transactions on Computers, 34(7), 663–666.

    Article  MathSciNet  Google Scholar 

  • Basu, A., & Li, X. (1991). A framework for variable-resolution vision. In Proceedings of the international conference on computing and information: advances in computing and information (pp. 721–732).

    Google Scholar 

  • Bay, H., Ess, A., Tuytelaars, T., & Gool, L. V. (2008). SURF: speeded up robust features. Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • Bonev, B., Cazorla, M., & Escolano, F. (2007). Robot navigation behaviors based on omnidirectional vision and information theory. Journal of Physical Agents, 1(1), 27–35.

    Google Scholar 

  • Brooks, J. C., & Owens, D. A. (2001). Effects of luminance, blur, and tunnel vision on postural stability. Journal of Vision, 1(3), 304.

    Article  Google Scholar 

  • Cho, M., & Lee, K. (2009). Bilateral symmetry detection via symmetry-growing. In Proceedings of the British machine vision conference.

    Google Scholar 

  • Choi, K., Bae, S., Lee, Y., & Park, C. (2003). A lateral position and orientation estimating algorithm for the navigation of the vision-based wheeled mobile robot in a corridor. In SICE annual conference (Vol. 3).

    Google Scholar 

  • Coombs, D., Herman, M., Hong, T.-H., & Nashman, M. (1998). Real-time obstacle avoidance using central flow divergence, and peripheral flow. IEEE Transactions on Robotics and Automation, 14(1), 49–59.

    Article  Google Scholar 

  • Corlett, J. (1992). The role of vision in the planning and guidance of locomotion through the environment. In L. Proteau & D. Elliot (Eds.), Advances in psychology—vision and motor control (pp. 375–397). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Escolano, F., Bonev, B., Suau, P., Aguilar, W., Frauel, Y., Saez, J., & Cazorla, M. (2007). Contextual visual localization: cascaded submap classification, optimized saliency detection, and fast view matching. In IEEE international conference on intelligent robots and systems.

    Google Scholar 

  • Hayashi, S., & Hasegawa, O. (2006). Detecting faces from low-resolution images. In Proceedings of the 7th Asian conference on computer vision (pp. 787–796).

    Google Scholar 

  • Hirschmüller, H. (2008). Stereo processing by semi-global matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 328–341.

    Article  Google Scholar 

  • Horn, B. K. P., Fang, Y., & Masaki, I. (2007). Time to contact relative to a planar surface. IEEE intelligent vehicles symposium (pp. 68–74).

  • Horswill, I. D. (1993). Polly: a vision-based artificial agent. In Proceedings of the national conference on artificial intelligence (pp. 824–829).

    Google Scholar 

  • Kim, J., Kolmogorov, V., & Zabih, R. (2003). Visual correspondence using energy minimization and mutual information. In Proceedings of the international conference on computer vision.

    Google Scholar 

  • Koenderink, J. J. (1984). The structure of images. Biological Cybernetics, 50(5), 363–370.

    Article  MathSciNet  MATH  Google Scholar 

  • Kogan, H., Maurer, R., & Keshet, R. (2009). Vanishing points estimation by self-similarity. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 755–761).

    Google Scholar 

  • Kong, H., Audibert, J.-Y., & Ponce, J. (2010). General road detection from a single image. IEEE Transactions on Image Processing, 19(8), 2211–2220.

    Article  MathSciNet  Google Scholar 

  • Kovesi, P. (1997). Symmetry and asymmetry from local phase. In Tenth Australian joint conference on artificial intelligence (pp. 185–190).

    Google Scholar 

  • Leibowitz, H. W., Rodemer, C. S., & Dichgans, J. (1979). The independence of dynamic spatial orientation from luminance and refractive error. Perception & Psychophysics, 25(2), 75–79.

    Article  Google Scholar 

  • Lindeberg, T. (1994). Scale-space theory: a basic tool for analysing structures at different scales. Journal of Applied Statistics, 21(2), 224–270.

    Google Scholar 

  • Lorigo, L. M., Brooks, R. A., & Grimson, W. E. L. (1997). Visually-guided obstacle avoidance in unstructured environments. In IEEE conference on intelligent robots and systems (Vol. 1) (pp. 373–379).

    Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability (pp. 281–297).

    Google Scholar 

  • McLean, G., & Kotturi, D. (1995). Vanishing point detection by line clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 1090–1095.

    Article  Google Scholar 

  • Murali, V. (2008). Autonomous navigation and mapping using monocular low-resolution grayscale vision. Master’s thesis, Clemson University.

  • Murali, V. (2011). Low-resolution vision for autonomous mobile robots. Ph.D. thesis, Clemson University.

  • Murali, V. N., & Birchfield, S. T. (2008). Autonomous navigation and mapping using monocular low-resolution grayscale vision. In Workshop on visual localization for mobile platforms (in association with CVPR).

    Google Scholar 

  • O’Kane, J. M., & LaValle, S. M. (2005). Almost-sensorless localization. In Proc. IEEE international conference on robotics and automation.

    Google Scholar 

  • Owens, D. A. (2003). Twilight vision and road safety. In J. Andre, D. A. Owens, & L. O. Harvey Jr. (Eds.), Visual perception: the influence of H.W. Leibowitz. Washington: American Psychological Association.

    Google Scholar 

  • Patla, A. E. (1997). Understanding the roles of vision in the control of human locomotion. Gait & Posture, 5(1), 54–69.

    Article  Google Scholar 

  • Pomerleau, D. A. (1991). Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3(1), 88–97.

    Article  Google Scholar 

  • Quan, L., & Mohr, R. (1989). Determining perspective structures using hierarchical Hough transform. Pattern Recognition Letters, 9(4), 279–286.

    Article  MATH  Google Scholar 

  • Russakoff, D. B., Tomasi, C., Rohlfing, T., & Maurer, C. R. (2004). Image similarity using mutual information of regions. In Proceedings of the 8th European conference on computer vision (pp. 596–607).

    Google Scholar 

  • Schneider, G. E. (1969). Two visual systems. Science, 163(3870), 895–902.

    Article  Google Scholar 

  • Stentiford, F. (2006). Attention-based vanishing point detection. In Proceedings of the IEEE international conference on image processing (pp. 417–420).

    Google Scholar 

  • Thomson, J. A. (1983). Is continuous visual monitoring necessary in visually guided locomotion? Journal of Experimental Psychology. Human Perception and Performance, 9(3), 427–443.

    Article  Google Scholar 

  • Torralba, A. (2009). How many pixels make an image? Visual Neuroscience, 26(1), 123–131.

    Article  MathSciNet  Google Scholar 

  • Torralba, A., Fergus, R., & Freeman, W. T. (2008). 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 1958–1970.

    Article  Google Scholar 

  • Torralba, A., & Sinha, P. (2001). Detecting faces in impoverished images. A.I. Memo 2001-28, MIT-AI.

  • Tovar, B., Guilamo, L., & Lavalle, S. M. (2004). Gap navigation trees: minimal representation for visibility-based tasks. In Proceedings of the workshop on the algorithmic foundations of robotics (pp. 11–26).

    Google Scholar 

  • Trevarthen, C. B. (1968). Two mechanisms of vision in primates. Psychologische Forschung, 31(4), 299–337.

    Article  Google Scholar 

  • Ulrich, I., & Nourbakhsh, I. (2000). Appearance-based place recognition for topological localization. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 1023–1029).

    Google Scholar 

  • Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.

    Article  Google Scholar 

  • Werner, J. S., & Chalupa, L. M. (2004). The visual neurosciences. Cambridge: MIT Press.

    Google Scholar 

  • Witkin, A. (1983). Scale space filtering. In Proceedings of the international joint conference on artificial intelligence.

    Google Scholar 

  • Zabrodsky, H., Peleg, S., & Avnir, D. (1995). Symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(12), 1154–1166.

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Clemson University, Clemson, SC, 29634, USA

    Vidya N. Murali & Stanley T. Birchfield

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  1. Vidya N. Murali
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  2. Stanley T. Birchfield
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Correspondence to Stanley T. Birchfield.

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Murali, V.N., Birchfield, S.T. Autonomous exploration using rapid perception of low-resolution image information. Auton Robot 32, 115–128 (2012). https://doi.org/10.1007/s10514-011-9262-z

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