RGB-D based cognitive map building and navigation

This paper describes a cognitive map building and navigation system using an RGB-D sensor for mobile robots. A brain-inspired simultaneously localization and mapping (SLAM) system, that requires raw odometry data and RGB-D information, is used to construct a spatial cognitive map of an office environment. The cognitive map contains a set of spatial coordinates that the robot has traveled. A global path is extracted from the built cognitive map and subsequently used by a local planner to instruct the robot to navigate. The global path is a subset of the path that builds up the cognitive map. This is different from other path planning mechanisms that construct a path based on a ground-truth map. Experiment results show that the employment of the RGB-D sensor significantly improves the mapping results.

[1]  Gamini Dissanayake,et al.  A robust RGB-D SLAM algorithm , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  B L McNaughton,et al.  Path Integration and Cognitive Mapping in a Continuous Attractor Neural Network Model , 1997, The Journal of Neuroscience.

[3]  Bruce L. McNaughton,et al.  Path integration and the neural basis of the 'cognitive map' , 2006, Nature Reviews Neuroscience.

[4]  Kay Chen Tan,et al.  A neuro-cognitive system and its application in robotics , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[5]  R. Passingham.  The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.

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

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

[8]  Dieter Fox,et al.  RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments , 2010, ISER.

[9]  Paul Newman,et al.  Appearance-only SLAM at large scale with FAB-MAP 2.0 , 2011, Int. J. Robotics Res..

[10]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

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

[12]  Weiwei Huang,et al.  Brain Inspired Cognitive System for Learning and Memory , 2011, ICONIP.

[13]  Paul Newman,et al.  FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance , 2008, Int. J. Robotics Res..

[14]  Gordon Wyeth,et al.  FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day , 2010, 2010 IEEE International Conference on Robotics and Automation.

[15]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[16]  Weiwei Huang,et al.  A Neuro-cognitive Robot for Spatial Navigation , 2011, ICONIP.

[17]  T. Hafting,et al.  Microstructure of a spatial map in the entorhinal cortex , 2005, Nature.

[18]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

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

[20]  E. Tolman.  Cognitive maps in rats and men. , 1948, Psychological review.