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IS-CAT: Intensity-Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition.

Hyeong-Jun Joo1, Jaeho Kim2

  • 1Department of Information and Communications Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
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This study introduces a novel LiDAR approach for robust place recognition in autonomous navigation. The intensity and spatial cross-attention transformer (IS-CAT) fuses spatial and intensity data for superior performance in diverse environments.

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • LiDAR place recognition is vital for autonomous navigation and Simultaneous Localization and Mapping (SLAM).
  • LiDAR offers robustness in challenging environments where camera-based methods falter due to weather or lighting variations.

Purpose of the Study:

  • To introduce a novel LiDAR-based approach for enhanced place recognition.
  • To explore the synergy between spatial and intensity data in LiDAR for global descriptor generation.

Main Methods:

  • Developed the intensity and spatial cross-attention transformer (IS-CAT) model.
  • Utilized a cross-attention to concatenation mechanism to integrate multi-layered LiDAR projections.
  • Fused spatial and intensity LiDAR data for comprehensive place representation.
Keywords:
IS-CATLiDAR place recognitionSLAMcross-attention transformer network

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Main Results:

  • IS-CAT demonstrated superior performance in place recognition tasks on the NCLT and Sejong indoor-5F datasets.
  • The model showed successful application within a 3D LiDAR SLAM system.
  • Achieved enhanced performance in both indoor and outdoor environments.

Conclusions:

  • The proposed IS-CAT method effectively fuses spatial and intensity LiDAR data for advanced place recognition.
  • This approach offers practical effectiveness and significant advancements for autonomous navigation systems.
  • The findings underscore the value of integrating multi-modal LiDAR data for robust localization.