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A neuro-inspired visual SLAM approach using AKAZE feature extraction in complex and dynamic environments.

Ruibang Li1, Yihong Wang1,2, Xuying Xu1,2

  • 1Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, 200237 China.

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|December 9, 2025
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Summary

This study enhances visual simultaneous localization and mapping (SLAM) by integrating the AKAZE algorithm into RatSLAM, improving navigation in complex environments. The new system, AKAZE-RatSLAM, offers greater accuracy and efficiency while maintaining biologically plausible spatial representations.

Keywords:
AKAZE featuresBrain-inspired navigationLoop closure detectionRatSLAMSimultaneous localization and mapping (SLAM)

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Neuroscience
  • Computer Vision

Background:

  • Rodent brains use place and head direction cells for navigation.
  • RatSLAM simulates these cells for biologically inspired visual SLAM.
  • Traditional RatSLAM faces challenges with feature extraction in complex environments.

Purpose of the Study:

  • To improve RatSLAM's robustness and accuracy in visually challenging environments.
  • To introduce a new evaluation method for SLAM mapping performance.
  • To assess the neurobiological plausibility of the enhanced system.

Main Methods:

  • Integrated the AKAZE algorithm for robust feature extraction into RatSLAM.
  • Developed a novel Ray-Based Map Metric Error Evaluation Method.
  • Evaluated performance on the KITTI dataset and compared with ORB-RatSLAM and ORB-SLAM3.

Main Results:

  • AKAZE-RatSLAM demonstrated superior loop closure recall and mapping accuracy.
  • The system achieved higher efficiency with reduced computational resource usage.
  • Neuro-inspired analysis confirmed spatially localized and direction-selective firing patterns.

Conclusions:

  • AKAZE-RatSLAM significantly enhances visual SLAM performance and efficiency.
  • The system maintains neurobiological plausibility, advancing brain-inspired robotics.
  • The proposed evaluation method provides more accurate mapping assessments.