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Modeling the Functional Network for Spatial Navigation in the Human Brain
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A brain-inspired compact cognitive mapping system.

Taiping Zeng1,2, Bailu Si3

  • 1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Cognitive Neurodynamics
|March 31, 2021
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Summary
This summary is machine-generated.

This study introduces a compact cognitive mapping method for simultaneous localization and mapping (SLAM) systems. The approach effectively limits map size growth, enabling long-term robot navigation in large environments.

Keywords:
Compact cognitive mapLong-term mappingNeighborhood cellsNeighborhood fieldsSLAM

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Neuroscience

Background:

  • Simultaneous localization and mapping (SLAM) systems face challenges with ever-growing maps, hindering long-term operation, especially in large-scale environments.
  • Existing SLAM methods struggle to manage map size, limiting their applicability for extended autonomous navigation.

Purpose of the Study:

  • To develop a compact cognitive mapping approach inspired by neurobiology to address the limitations of growing maps in SLAM.
  • To enable efficient long-term mapping and robot navigation in large-scale environments.

Main Methods:

  • Modeled neighborhood fields based on movement information (translation, rotation) to segment environments, inspired by neurobiological cell firing.
  • Formulated cognitive map optimization as a non-linear least squares problem with constraints on vertex transitions.
  • Implemented loop closure clustering based on time intervals and performed batch global optimization, followed by scene integration to remove revisited vertices.

Main Results:

  • The proposed compact cognitive mapping method significantly restricted the growth of the cognitive map size over time.
  • The compact cognitive map accurately represented the overall layout of the explored environment.
  • The approach was successfully tested on a monocular visual SLAM system in a naturalistic maze for a biomimetic robot.

Conclusions:

  • The compact cognitive mapping approach effectively manages map size, making it suitable for long-term robot navigation in large-scale environments.
  • This neurobiologically inspired method offers a robust solution for efficient and compact environmental representation in SLAM.