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Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot.

Xin Zhang1,2,3, Liaomo Zheng2, Zhenhua Tan3

  • 1School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China.

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Summary

This study introduces a novel loop closure detection method for mobile robot simultaneous localization and mapping (SLAM) using a fused residual network (ResNet) and capsule network (CapsNet). The approach enhances accuracy and robustness in complex environments.

Keywords:
capsule network (CapsNet)loop closure detectionmobile robotresidual network (ResNet)simultaneous localization and mapping (SLAM)

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Mobile robot simultaneous localization and mapping (SLAM) faces challenges in complex scenes, leading to low accuracy and poor robustness.
  • Existing methods struggle with issues like view changes, illumination variations, and dynamic objects.

Purpose of the Study:

  • To propose an improved loop closure detection method for mobile robot SLAM.
  • To enhance the accuracy and robustness of SLAM in complex environments using deep learning techniques.

Main Methods:

  • A feature coding strategy using a residual network (ResNet) to extract shallow geometric and deep semantic features, reducing noise and improving convergence.
  • Optimization of the capsule network (CapsNet) dynamic routing mechanism using entropy peak density for better feature representation.
  • Fusion of ResNet and CapsNet to leverage feature differences and correlations, combined with global feature descriptors and vectors for similarity calculation.

Main Results:

  • The proposed method effectively performs loop closure detection in challenging conditions, including changes in viewpoint, illumination, and the presence of dynamic objects.
  • Demonstrated significant improvements in the accuracy and robustness of mobile robot SLAM.

Conclusions:

  • The fused ResNet and CapsNet approach provides a robust solution for loop closure detection in mobile robot SLAM.
  • The method effectively addresses limitations of existing techniques in complex and dynamic environments.