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A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds.

Ricardo Roriz1, Heitor Silva1, Francisco Dias1

  • 1Centro ALGORITMI/LASI, Escola de Engenharia, Universidade do Minho, 4800-058 Guimarães, Portugal.

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Autonomous driving relies on Light Detection and Ranging (LiDAR) sensors for perception. This survey reviews point cloud compression methods for automotive LiDAR, categorizing techniques to address large data volumes.

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Area of Science:

  • Robotics and Intelligent Systems
  • Computer Vision and Sensor Fusion
  • Data Compression and Signal Processing

Background:

  • Light Detection and Ranging (LiDAR) sensors are crucial for autonomous driving, providing high-resolution 3D environmental perception.
  • LiDAR's benefits include precise measurements and long-range capabilities, even in low-light conditions.
  • The large data volume from LiDAR sensors presents significant transmission, processing, and storage challenges.

Purpose of the Study:

  • To survey and categorize existing data compression methods for automotive LiDAR point cloud data.
  • To provide a comprehensive taxonomy of LiDAR compression techniques.
  • To compare and discuss these methods based on key performance metrics.

Main Methods:

  • Literature review of existing point cloud compression techniques for automotive LiDAR.
  • Development of a taxonomy to classify compression approaches into four main groups.
  • Comparative analysis of categorized methods across relevant metrics.

Main Results:

  • Identification and categorization of key point cloud compression strategies for LiDAR data.
  • Discussion of the trade-offs and effectiveness of different compression approaches.
  • Highlighting the importance of compression for efficient LiDAR data management.

Conclusions:

  • Data compression is essential for overcoming the challenges posed by large LiDAR datasets in autonomous vehicles.
  • The presented taxonomy offers a structured overview of current compression methods.
  • Further research and development in efficient LiDAR compression are vital for advancing autonomous driving technology.