Bundzel M, Hashimoto S. Object Identification in Dynamic Images Based on the Memory-Prediction Theory of Brain Function. Journal of Intelligent Learning Systems and Applications, 2010, 2(4): 212-220
[2]
Yang J, Jiang Y G, Hauptmann A G, et al. Evaluating Bag-of-Visual-Words Representations in Scene Classification // Proc of the International Workshop on Multimedia Information Retrieval. Augsburg, Germany, 2007: 197-206
[3]
Grauman K, Leibe B. Visual Object Recognition. San Rafael, USA: Morgan & Claypool Publisher, 2011
[4]
Milford M, Schulz R, Prasser D, et al. Learning Spatial Concepts from RatSLAM Representations. Robotics and Autonomous Systems, 2007, 55(5): 403-410
[5]
Bacca B, Salvi J, Cufí X. Appearance-Based SLAM for Mobile Robots // Proc of the 12th International Conference of the Catalan Association for Artificial Intelligence. Cardona, Spain, 2009: 55-64
[6]
Cummins M, Newman P. FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. The International Journal of Robotics Research, 2008, 27(6): 647-665
[7]
Cummins M, Newman P. Appearance-Only SLAM at Large Scale with FAB-MAP 2.0. The International Journal of Robotics Research, 2011, 30(9): 1100-1123
[8]
Beeson P, Modayil J, Kuipers B. Factoring the Mapping Problem: Mobile Robot Map-Building in the Hybrid Spatial Semantic Hierarchy. The International Journal of Robotics Research, 2010, 29(4): 428-459
[9]
Wu P L, Kong L F, Gao S N. Holography Map for Home Robot: An Object-Oriented Approach. Intelligent Service Robotics, 2012, 5(3): 147-157
[10]
Barrera A, Weitzenfeld A. Biologically-Inspired Robot Spatial Cognition Based on Rat Neurophysiological Studies. Autonomous Robots, 2008, 25(1/2): 147-169
[11]
Milford M J, Wyeth G F. Mapping a Suburb with a Single Camera Using a Biologically Inspired SLAM System.Trans on Robotics, 2008, 24(5): 1038-1053
[12]
Wyeth G, Milford M. Spatial Cognition for Robots.Robotics & Automation Magazine, 2009, 16(3): 24-32
[13]
Rebai K, Azouaoui O, Achour N. Fuzzy ART-Based Place Recognition for Visual Loop Closure Detection. Biological Cybernetics, 2013, 107(2): 247-259
[14]
Li Y M, Li S, Ge Y J. A Biologically Inspired Solution to Simultaneous Localization and Consistent Mapping in Dynamic Environments. Neurocomputing, 2013, 104: 170-179
[15]
Hawkins J, Blakeslee S. On Intelligence. New York, USA: Henry Holt and Company, 2004
[16]
Zhang X Z, Zhang J F, Rad A B, et al. A Novel Mapping Strategy Based on Neocortex Model: Pre-liminary Results by Hierarchical Temporal Memory // Proc of theInternational Conference on Robotics and Biomimetics. Guangzhou, China, 2012: 476-481
[17]
Hawkins J, Ahmad S, Dubinsky D. Hierarchical Temporal Memory Including HTM Cortical Learning Algorithms [EB/OL]. [2013-12-20]. http:// numenta.com/assets/pdf/whitepapers/hierarchical-temporal-memory-cortical-learning-algorithm-0.2.1-en.pdf
[18]
Kawewong A, Tongprasit N, Tangruamsub S, et al. Online and Incremental Appearance-Based SLAM in Highly Dynamic Environments. The International Journal of Robotics Research, 2011, 30(1): 33-55
[19]
Kawewong A, Tongprasit N, Hasegawa O. PIRF-Nav 2.0: Fast and Online Incremental Appearance-Based Loop-Closure Detection in an Indoor Environment. Robotics and Autonomous Systems, 2011, 59(10): 727-739
[20]
George D. How the Brain Might Work: A Hierarchical and Temporal Model for Learning and Recognition. Ph.D Dissertation. Stanford, USA: Stanford University, 2008
