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A Multi-Sensor Fusion Approach Combined with RandLA-Net for Large-Scale Point Cloud Segmentation in Power Grid

Tianyi Li1, Shuanglin Li1, Zihan Xu1

  • 1College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-sensor fusion and deep learning approach for intelligent power grid tower recognition. The method enhances inspection efficiency and safety by accurately processing large-scale point cloud data.

Keywords:
3D scene understandingLiDARRandLA-Netdeep learningdigital twinmulti-sensor fusionpoint cloud segmentationpower grid tower

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Robotics

Background:

  • Traditional power grid tower inspections are inefficient, costly, and pose safety risks.
  • Existing methods for analyzing large-scale point cloud data in complex environments lack accuracy and efficiency.
  • Intelligent recognition and monitoring are crucial for the stable operation of power grids.

Purpose of the Study:

  • To develop an advanced method for power grid tower recognition using multi-sensor fusion and deep learning.
  • To address the limitations of current techniques in processing large-scale, complex point cloud data.
  • To improve the efficiency, accuracy, and safety of power grid infrastructure inspection.

Main Methods:

  • Proposed a data acquisition scheme integrating LiDAR and a binocular depth camera with the FAST-LIO algorithm for spatiotemporal synchronization and data fusion.
  • Constructed a colored point cloud dataset with rich visual and geometric features.
  • Developed and optimized an efficient point cloud segmentation method based on the RandLA-Net framework for power grid tower scenarios.

Main Results:

  • Achieved 90.8% precision in power grid tower body recognition.
  • Demonstrated robust performance across diverse environmental conditions.
  • Successfully processed point cloud data exceeding ten million points, handling uneven distribution and interference.

Conclusions:

  • The proposed multi-sensor fusion and deep learning approach provides a reliable method for intelligent power grid tower recognition.
  • This technique offers significant improvements in accuracy and efficiency for large-scale point cloud data analysis.
  • The findings support the development of intelligent inspection and management systems for power grid infrastructure.