Impact of Neuroscience in Robotic Vision Localization and Navigation

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.

[1]  M. Potter.  Meaning in visual search. , 1975, Science.

[2]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[3]  I Biederman,et al.  Do Background Depth Gradients Facilitate Object Identification? , 1981, Perception.

[4]  B. Tversky,et al.  Categories of environmental scenes , 1983, Cognitive Psychology.

[5]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  G. Sperling,et al.  Drift-balanced random stimuli: a general basis for studying non-Fourier motion perception. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[7]  T. McNamara.  Memory's view of space , 1991 .

[8]  Oussama Khatib,et al.  Elastic bands: connecting path planning and control , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[9]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[10]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[11]  W. Epstein,et al.  Priming Spatial Layout of Scenes , 1997 .

[12]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[13]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[14]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[15]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[16]  Russell A. Epstein,et al.  The Parahippocampal Place Area Recognition, Navigation, or Encoding? , 1999, Neuron.

[17]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[18]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[19]  Ronald A. Rensink.  The Dynamic Representation of Scenes , 2000 .

[20]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[21]  Masayuki Inaba,et al.  View-based approach to robot navigation , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[22]  A. Oliva,et al.  Diagnostic Colors Mediate Scene Recognition , 2000, Cognitive Psychology.

[23]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[24]  Rafael Murrieta-Cid,et al.  Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model , 2002, Auton. Robots.

[25]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[26]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Yoshiaki Shirai,et al.  A view-based outdoor navigation using object recognition robust to changes of weather and seasons , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[28]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[29]  Antonio Torralba,et al.  Modeling global scene factors in attention. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[30]  Laurent Itti,et al.  Realistic avatar eye and head animation using a neurobiological model of visual attention , 2004, SPIE Optics + Photonics.

[31]  Barbara Tversky,et al.  Navigating by Mind and by Body , 2003, Spatial Cognition.

[32]  Benjamin Kuipers,et al.  Local metrical and global topological maps in the hybrid spatial semantic hierarchy , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[33]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[34]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[35]  Pietro Perona,et al.  Is bottom-up attention useful for object recognition? , 2004, CVPR 2004.

[36]  Jitendra Malik,et al.  When is scene identification just texture recognition? , 2004, Vision Research.

[37]  Wei Zhang,et al.  Localization Based on Building Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[38]  James J. Little,et al.  Vision-based global localization and mapping for mobile robots , 2005, IEEE Transactions on Robotics.

[39]  Paolo Pirjanian,et al.  A Visual Front-end for Simultaneous Localization and Mapping , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[40]  S. Thorpe,et al.  Rapid categorization of achromatic natural scenes: how robust at very low contrasts? , 2005, The European journal of neuroscience.

[41]  Laurent Itti,et al.  Robot steering with spectral image information , 2005, IEEE Transactions on Robotics.

[42]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[43]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[44]  Hongbin Zha,et al.  Coarse-to-fine vision-based localization by indexing scale-Invariant features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[46]  Henrik I. Christensen,et al.  Attentional Landmark Selection for Visual SLAM , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[47]  Javier González,et al.  Consistent observation grouping for generating metric-topological maps that improves robot localization , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[48]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Laurent Itti,et al.  Biologically-inspired robotics vision monte-carlo localization in the outdoor environment , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[50]  Laurent Itti,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Rapid Biologically-inspired Scene Classification Using Features Shared with Visual Attention , 2022 .

[51]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Benjamin Kuipers,et al.  An Intellectual History of the Spatial Semantic Hierarchy , 2008, Robotics and Cognitive Approaches to Spatial Mapping.

[53]  Gordon Wyeth,et al.  Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System , 2008, IEEE Transactions on Robotics.

[54]  Nuno Vasconcelos,et al.  On the plausibility of the discriminant center-surround hypothesis for visual saliency. , 2008, Journal of vision.

[55]  Adriana Tapus,et al.  Mobile robot localization using panoramic vision and combinations of feature region detectors , 2008, 2008 IEEE International Conference on Robotics and Automation.

[56]  Achim J. Lilienthal,et al.  Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments , 2008, 2008 IEEE International Conference on Robotics and Automation.

[57]  Tim K Marks,et al.  SUN: A Bayesian framework for saliency using natural statistics. , 2008, Journal of vision.

[58]  Dirk Haehnel,et al.  Junior: The Stanford entry in the Urban Challenge , 2008 .

[59]  Laurent Itti,et al.  Storing and recalling information for vision localization , 2008, 2008 IEEE International Conference on Robotics and Automation.

[60]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[61]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

[62]  Laurent Itti,et al.  Biologically Inspired Mobile Robot Vision Localization , 2009, IEEE Transactions on Robotics.

[63]  Paul L. Rosin.  A simple method for detecting salient regions , 2009, Pattern Recognit..

[64]  Nathalie Guyader,et al.  Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos , 2009, International Journal of Computer Vision.

[65]  Andreas Krause,et al.  Unfreezing the robot: Navigation in dense, interacting crowds , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[66]  Gordon Wyeth,et al.  Persistent Navigation and Mapping using a Biologically Inspired SLAM System , 2010, Int. J. Robotics Res..

[67]  V. Nuno.  On the connections between SIFT and biological vision , 2010 .

[68]  Christian Vollmer,et al.  Learning to navigate through crowded environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[69]  Kurt Konolige,et al.  The Office Marathon: Robust navigation in an indoor office environment , 2010, 2010 IEEE International Conference on Robotics and Automation.

[70]  Esa Rahtu,et al.  Segmenting Salient Objects from Images and Videos , 2010, ECCV.

[71]  Benjamin Kuipers,et al.  Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy , 2010, Int. J. Robotics Res..

[72]  Gérard G. Medioni,et al.  Robot vision for the visually impaired , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[73]  Frank Dellaert,et al.  Online probabilistic topological mapping , 2011, Int. J. Robotics Res..

[74]  Laurent Itti,et al.  Beobot 2.0: Cluster architecture for mobile robotics , 2011, J. Field Robotics.

[75]  Kurt Konolige,et al.  Navigation in hybrid metric-topological maps , 2011, 2011 IEEE International Conference on Robotics and Automation.

[76]  Laurent Itti,et al.  Mobile robot monocular vision navigation based on road region and boundary estimation , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[77]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[78]  Laurent Itti,et al.  Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition , 2013, 2013 IEEE International Conference on Robotics and Automation.