RatSLAM: a hippocampal model for simultaneous localization and mapping

@article{Milford2004RatSLAMAH,
  title={RatSLAM: a hippocampal model for simultaneous localization and mapping},
  author={Michael Milford and Gordon Wyeth and David Prasser},
  journal={IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004},
  year={2004},
  volume={1},
  pages={403-408 Vol.1}
}
The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. [] Key Method It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.
Simultaneous localisation and mapping from natural landmarks using RatSLAM
TLDR
The effectiveness of RatSLAM, a Simultaneous Localisation and Mapping system based on models of the rodent hippocampus, is shown in real robot tests in unmodified indoor environments using a learning vision system.
RatSLAM on the Edge: Revealing a Coherent Representation from an Overloaded Rat Brain
TLDR
A new component for the RatSLAM system is described, which provides a coherent representation for goal directed navigation and the ability of the experience map to adapt to simple short term changes in the environment.
Effect of representation size and visual ambiguity on RatSLAM system performance
TLDR
This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance and demonstrates that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.
A Bio-Inspired Model of Navigation in a Multi-Chamber Maze
  • M. Bureš, M. Jirina
  • Biology, Psychology
    2006 IEEE International Conference on Computational Cybernetics
  • 2006
TLDR
A model of spatial memory inspired by rodent hippocampus, which provides a robust and efficient method of storing several navigational maps in a single attractor neural (Hopfield-like) network, could provide a potential foundation for a future robotic use.
RatSLAM: Using Models of Rodent Hippocampus for Robot Navigation and Beyond
We describe recent biologically-inspired mapping research incorporating brain-based multi-sensor fusion and calibration processes and a new multi-scale, homogeneous mapping framework. We also review
Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields
TLDR
This paper develops a self-organized model incorporating place cells and replay, and demonstrates its utility for rapid one-shot learning in non-trivial environments with obstacles.
A Model of Navigation in a Complex Maze Inspired by Hippocampus
TLDR
A model of navigation inspired by rodent hippocampus stores several navigational maps in a single attractor (Hopfield-like) neural network and structure of the model conforms to functional schema of hippocampal formation.
Biologically inspired visual landmark processing for simultaneous localization and mapping
TLDR
A method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM) using layers of filtering and pooling to create learned templates that correspond to different views of the environment.
A Bionic Robot Navigation Algorithm Based on Cognitive Mechanism of Hippocampus
TLDR
A bionic robot navigation algorithm based on the cognitive mechanism of rat hippocampus that makes mobile robots more intelligent by learning and imitating human and animal's environmental cognition and navigation ability is proposed.
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References

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Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input, support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.
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TLDR
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