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Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments.

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This summary is machine-generated.

This study introduces a scalable embedded system for processing 3D point cloud data. The solution efficiently compresses and transfers data from multiple sensors, enhancing scalability in industrial applications.

Keywords:
3D sensorscompressiondenoisinglidarpoint cloudsscalabilitytime-of-flight

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Area of Science:

  • Computer Vision
  • Robotics
  • Embedded Systems

Background:

  • 3D point cloud data acquisition is crucial for industrial automation.
  • Efficient processing and transfer of large 3D datasets present significant challenges.
  • Existing solutions often lack scalability for large-scale deployments.

Purpose of the Study:

  • To present a scalable embedded solution for processing and transferring 3D point cloud data.
  • To enable efficient data handling from multiple time-of-flight sensors in industrial environments.
  • To demonstrate the scalability and effectiveness of local data processing and compression.

Main Methods:

  • Utilizing time-of-flight sensors for 3D data acquisition.
  • Implementing an octree-based compression scheme on local embedded computers.
  • Developing a novel method for generating intensity values for data filtering.
  • Decompressing and filtering point clouds at a central node.

Main Results:

  • Experimental validation in a 10m x 10m x 4m industrial robot cell.
  • Demonstrated scalability up to approximately 440 sensor nodes with a Gigabit Ethernet network.
  • Achieved a compression ratio of 40.5 for a single Microsoft Kinect V2 sensor stream.
  • Successful filtering and integration of data from multiple nodes.

Conclusions:

  • Local processing and octree-based compression significantly enhance the scalability of 3D point cloud data handling.
  • The proposed embedded solution is effective for large-scale industrial applications.
  • The novel intensity value generation method improves data quality for sensors lacking native intensity output.