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Impact of Neuroscience in Robotic Vision Localization and Navigation

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Computational and Cognitive Neuroscience of Vision

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Abstract

One important reason to examine the mechanisms of how we see is for the advancement of technology. Robotics as a field has a distinct appeal because its goal is to build fully functioning intelligent systems that can operate robustly in the real world.

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References

  • Achanta R, Hemami S, Estrada F, Ssstrunk S (2009) Frequency-tuned salient region detection. In: IEEE international conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  • Ackerman C, Itti L (2005) Robot steering with spectral image information. IEEE Trans Robot 21:247–251

    Article  Google Scholar 

  • American Honda Motor Co Inc. (2009). Asimo—the world’s most advanced humanoid robot. http://asimo.honda.com/. Accessed 15 July 2009

  • Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: Proceedings of European conference on computer vision (ECCV), pp 404–417

    Google Scholar 

  • Beeson P, Modayil J, Kuipers B (2010) Factoring the mapping problem: mobile robot map-building in the hybrid spatial semantic hierarchy. Int J Robot Res 29:428–459

    Article  Google Scholar 

  • Benenson R, Mathias M, Timofte R, Gool LV (2012) Pedestrian detection at 100 frames per second. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Providence, RI, USA, pp 290–2910

    Google Scholar 

  • Biederman I (1982) Do background depth gradients facilitate object identification? Perception 10:573–578

    Article  Google Scholar 

  • Bruce N, Tsotsos J (2006) Saliency based on information maximization. In: Weiss Y, Scholkopf JPB (eds) Advances in neural information processing systems, vol 18. MIT Press, Cambridge, MA, USA, pp 155–162

    Google Scholar 

  • Blanco JL, Gonzalez J, Fernndez-Madrigal JA (2006) Consistent observation grouping for generating metric- topological maps that improves robot localization*. In: ICRA. Barcelona, Spain

    Google Scholar 

  • Chang CK, Siagian C, Itti L (2012) Mobile robot monocular vision navigation based on road region and boundary estimation. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1043–1050

    Google Scholar 

  • Chubb C, Sperling G (1988) Drift-balanced random stimuli: a general basis for studying non-Fourier motion perception. J Opt Soc Am 5:1986–2007

    Article  MathSciNet  Google Scholar 

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR

    Google Scholar 

  • Epstein R, Stanley D, Harris A, Kanwisher N (2000) The parahippocampal place area: perception, encoding, or memory retrieval? Neuron 23:115–125

    Article  Google Scholar 

  • Frintrop S, Jensfelt P, Christensen H (2006) Attention landmark selection for visual slam. In: IROS. Beijing

    Google Scholar 

  • Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4:23–33

    Article  Google Scholar 

  • Fox D, Burgard W, Dellaert F, Thrun S (1999) Monte carlo localization: efficient position estimation for mobile robots. In: Proceedings of sixteenth national conference on artificial intelligence (AAAI’99)

    Google Scholar 

  • Gao D, Mahadevan V, Vasconcelos N (2008) On the plausibility of the discriminant center-surround hypothesis for visual saliency. J Vis 8:2301–2311

    Article  Google Scholar 

  • Goncalves L, Bernardo ED, Benson D, Svedman M, Ostrowski J et al (2005) A visual front-end for simultaneous localization and mapping. In: ICRA, pp 44–49

    Google Scholar 

  • Henry P, Vollmer C, Ferris B, Fox D (2010) Learning to navigate through crowded environments. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 981–986

    Google Scholar 

  • Hyvrinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10:626–634

    Article  Google Scholar 

  • Itti L (2012) iLab Neuromorphic Vision C++ Toolkit (iNVT). http://ilab.usc.edu/toolkit/. Accessed 15 Dec 2012

  • Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259

    Article  Google Scholar 

  • Itti L, Dhavale N, Pighin F (2003) Realistic avatar eye and head animation using a neurobiological model of visual attention. In: Bosacchi B, Fogel DB, Bezdek JC (eds) Proceedings of SPIE 48th annual international symposium on optical science and technology, vol 5200. SPIE Press, Bellingham, WA, pp 64–78

    Google Scholar 

  • Itti L, Koch C (2001) Computational modelling of visual attention. Nat Rev Neurosci 2:194–203

    Article  Google Scholar 

  • Katsura H, Miura J, Hild M, Shirai Y (2003) A view-based outdoor navigation using object recognition robust to changes of weather and seasons. In: IROS. Las Vegas, NV

    Google Scholar 

  • Kuipers B (2008) An intellectual history of the spatial semantic hierarchy. In: Jefferies M, Yeap AWK (eds) Robot and cognitive approaches to spatial mapping, vol 99. Springer, pp 21–71

    Google Scholar 

  • Kuipers B, Modayil J, Beeson P, Macmahon M, Savelli F (2004) Local metrical and global topological maps in the hybrid spatial semantic hierarchy. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 4845–4851

    Google Scholar 

  • Liu T, Yuan Z, Sun J, Wang J, Zheng N et al (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33:353–367

    Article  Google Scholar 

  • Li F, VanRullen R, Koch C, Perona P (2002) Rapid natural scene categorization in the near absence of attention. In: Proceedings of National Academy of Science, pp 8378–8383

    Google Scholar 

  • Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  • Macé MJ, Thorpe SJ, Fabre-Thorpe M (2005) Rapid categorization of achromatic natural scenes: how robust at very low contrasts? Eur J Neurosci 21:2007–2018

    Google Scholar 

  • Marat S, Phuoc TH, Granjon L, Guyader N, Pellerin D et al (2009) Modelling spatio-temporal saliency to predict gaze direction for short videos. Int J Comput Vis 82:231–243

    Article  Google Scholar 

  • Marder-Eppstein E, Berger E, Foote T, Gerkey B, Konolige K (2010) The office marathon: robust navigation in an indoor office environment. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 300–307

    Google Scholar 

  • Marder-Eppstein E, Berger E, Foote T, Gerkey B, Konolige K (2011) Kurt Konolige, Eitan Marder-Eppstein, Bhaskara Marthi. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 3041–3047

    Google Scholar 

  • Matsumoto Y, Inaba M, Inoue H (2000) View-based approach to robot navigation. In: IEEE-IROS, pp 1702–1708

    Google Scholar 

  • McNamara TP (1991) Memory’s view of space. In: Bower GH (ed) The psychology of learning and motivation: advances in research and theory, vol 27. Academic Press, pp 147–186

    Google Scholar 

  • Milford M, Wyeth G (2008) Mapping a suburb with a single camera using a biologically inspired slam system. IEEE Trans Robot 24:1038–1053

    Article  Google Scholar 

  • Milford M, Wyeth G (2010) Persistent navigation and mapping using a biologically inspired slam system. Int J Robot Res (IJRR) 29:1131–1153

    Article  Google Scholar 

  • Montemerlo M, Becker J, Bhat S, Dahlkamp H, Dolgov D et al (2008) Junior: the stanford entry in the urban challenge. J Field Robot 25:569–597

    Article  Google Scholar 

  • Montemerlo M, Thrun S, Koller D, Wegbreit B (2002) Fastslam: a factored solution to the simultaneous localization and mapping problem. In: AAAI

    Google Scholar 

  • Moravec H, Elfes A (1985) High resolution maps from wide angle sonar. In: Proceedings of IEEE international conference on robotics and automation (ICRA), vol 2, pp 116–121

    Google Scholar 

  • Muralidharan K, Vasconcelos N (2006) A biologically plausible network for the computation of orientation dominance. In: Weiss Y, Scholkopf JPB (eds) Advances in neural information processing systems (NIPS), vol 18. MIT Press, Cambridge, MA, USA, pp 155–162

    Google Scholar 

  • Muralidharan K, Vasconcelos N (2010) On the connections between sift and biological vision. Front Syst Neurosci

    Google Scholar 

  • Murrieta-Cid R, Parra C, Devy M (2002) Visual navigation in natural environments: from range and color data to a landmark-based model. Auton Robots 13:143–168

    Article  MATH  Google Scholar 

  • Oliva A, Schyns P (1997) Coarse blobs or fine edges? evidence that information diagnosticity changes the perception of complex visual stimuli. Cogn Psychol 34:72–107

    Article  Google Scholar 

  • Oliva A, Schyns P (2000) Colored diagnostic blobs mediate scene recognition. Cogn Psychol 41:176–210

    Article  Google Scholar 

  • Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42:145–175

    Article  MATH  Google Scholar 

  • Potter MC (1975) Meaning in visual search. Science 187:965–966

    Article  Google Scholar 

  • Pradeep V, Medioni G, Weiland J (2010) Robot vision for the visually impaired. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, pp 15–22

    Google Scholar 

  • Quinlan S, Khatib O (1993) Elastic bands: connecting path planning and control. In: Proceedings of IEEE international conference on robotics and automation (ICRA), pp 802–807

    Google Scholar 

  • Rahtu E, Kannala J, Salo M, Heikkil J (2010) Segmenting salient objects from images and videos. In: ECCV, pp 366–379

    Google Scholar 

  • Ramisa A, Tapus A, de Mantaras RL, Toledo R (2008) Mobile robot localization using panoramic vision and combination of local feature region detectors. In: ICRA. Pasadena, CA, pp 538–543

    Google Scholar 

  • Ranganathan A, Dellaert F (2011) Online probabilistic topological mapping. In: Int J Robot Res (IJRR) 30:755–771

    Google Scholar 

  • Renniger L, Malik J (2004) When is scene identification just texture recognition? Vis Res 44:2301–2311

    Article  Google Scholar 

  • Rensink RA (2000) The dynamic representation of scenes. Vis Cogn 7:17–42

    Article  Google Scholar 

  • Rosin PL (2009) A simple method for detecting salient regions. Pattern Recogn 42:2363–2371

    Article  MATH  Google Scholar 

  • Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: CVPR, vol 2, pp 37–44

    Google Scholar 

  • Sanocki T, Epstein W (1997) Priming spatial layout of scenes. Psychol Sci 8:374–378

    Article  Google Scholar 

  • Se S, Lowe DG, Little JJ (2005) Vision-based global localization and mapping for mobile robots. IEEE Trans Robot 21:364–375

    Article  Google Scholar 

  • Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T (2007) Robust object recognition with cortex-like mechanisms. IEEE Trans Pattern Anal Mach Intell 29:411–426

    Article  Google Scholar 

  • Siagian C, Chang CK, Voorhies R, Itti L (2011) Beobot 2.0: cluster architecture for mobile robotics. J Field Robot 28:278–302

    Article  Google Scholar 

  • Siagian C, Chang CK, Itti L (2013) Beobot 2.0. http://ilab.usc.edu/beobot2. Accessed 15 Dec 2012

  • Siagian C, Chang C, Itti L (2013) Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition. In: Proceedings of IEEE international conference on robotics and automation (ICRA). Both first authors contributed equally

    Google Scholar 

  • Siagian C, Itti L (2007) Biologically-inspired robotics vision Monte-Carlo localization in the outdoor environment. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS)

    Google Scholar 

  • Siagian C, Itti L (2008) Storing and recalling information for vision localization. In: IEEE international conference on robotics and automation (ICRA). Pasadena, California, pp 1848–1855

    Google Scholar 

  • Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29:300–312

    Article  Google Scholar 

  • Siagian C, Itti L (2009) Biologically inspired mobile robot vision localization. IEEE Trans Robot 25:861–873

    Article  Google Scholar 

  • Thorpe S, Fize D, Marlot C (1995) Speed of processing in the human visual system. Nature 381:520–522

    Article  Google Scholar 

  • Thrun S (2011) Google’s driverless car. Talk was viewed at http://www.ted.com/talks/sebastian_thrun_google_s_driverless_car.html. Accessed 1 Sept 2012

  • Thrun S (1998) Learning metric-topological maps for indoor mobile robot navigation. Artif Intell 99:21–71

    Article  MATH  Google Scholar 

  • Thrun S, Bennewitz M, Burgard W, Cremers A, Dellaert F et al (1999) MINERVA: a second generation mobile tour-guide robot. In: Proceedings of IEEE international conference on robotics and automation (ICRA)

    Google Scholar 

  • Torralba A (2003) Modeling global scene factors in attention. J Opt Soc Am 20:1407–1418

    Article  Google Scholar 

  • Torralba A, Murphy KP, Freeman WT, Rubin MA (2003) Context-based vision system for place and object recognition. In: Proceedings of international conference on computer vision (ICCV). Nice, France, pp 1023–1029

    Google Scholar 

  • Trautman P, Krause A (2010) Unfreezing the robot: navigation in dense, interacting crowds. In: Proceedings of IEEE international conference on intelligent robots and systems (IROS), pp 797–803

    Google Scholar 

  • Treisman A, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12:97–137

    Article  Google Scholar 

  • Turner RS (1994) In the eye’s mind: vision and the Helmholtz-Hering controversy. Princeton University Press

    Google Scholar 

  • Tversky B (2003) Navigating by mind and by body. In: Spatial cognition, pp 1–10

    Google Scholar 

  • Tversky B, Hemenway K (1983) Categories of the environmental scenes. Cogn Psychol 15:121–149

    Article  Google Scholar 

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

    Google Scholar 

  • Ungerleider LG, Mishkin M (1982) Two cortical visual systems. In: Ingle DJ, Goodale MA, Mansfield RJW (eds) Analysis of visual behavior. MIT Press, Cambridge, MA, pp 549–586

    Google Scholar 

  • Valgren C, Lilienthal AJ (2008) Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments. In: Proceedings of IEEE international conference on robotics and automation (ICRA). Pasadena, CA, pp 1856–1861

    Google Scholar 

  • Walther D, Koch C (2006) Modeling attention to salient proto-objects. Neural Netw 19:1395–1407

    Article  MATH  Google Scholar 

  • Wang J, Zha H, Cipolla R (2006) Coarse-to-fine vision-based localization by indexing scale-invariant features. IEEE Trans Syst Man Cybern 36:413–422

    Article  Google Scholar 

  • Willow Garage (2009) PR-2—Wiki. http://pr.willowgarage.com/wiki/PR-2. Accessed 15 July 2009

  • Wolfe J (1994) Guided search 2.0: a revised model of visual search. Psychon Bull Rev 1:202–238

    Article  Google Scholar 

  • Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) Sun: a Bayesian framework for saliency using natural statistics. J Vis 8:231–243

    Google Scholar 

  • Zhang W, Kosecka J (2005) Localization based on building recognition. In: IEEE workshop on applications for visually impaired, pp 21–28

    Google Scholar 

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Acknowledgments

This work was supported by the National Science Foundation (grant numbers CCF-1317433 and CNS-1545089), the Army Research Office (W911NF-12-1-0433), and the Office of Naval Research (N00014-13-1-0563). The authors affirm that the views expressed herein are solely their own, and do not represent the views of the United States government or any agency thereof.

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Siagian, C., Itti, L. (2017). Impact of Neuroscience in Robotic Vision Localization and Navigation. In: Zhao, Q. (eds) Computational and Cognitive Neuroscience of Vision. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0213-7_11

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