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LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors.

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Summary

This study introduces LiDAR-Net, a convolutional neural network that enhances Light Detection and Ranging (LiDAR) intensity maps by fusing depth and intensity data. This improves computer vision applications for autonomous systems.

Keywords:
LiDAR intensity completionLiDAR sensorsintensity normalization

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Light Detection and Ranging (LiDAR) offers active depth and intensity measurements, robust to ambient light conditions, surpassing visual cameras in certain aspects.
  • Current LiDAR systems underutilize intensity data due to sparse and non-standard map outputs, limiting the application of established computer vision techniques.
  • Bridging the gap between LiDAR's sparse intensity data and the requirements of traditional computer vision algorithms is crucial for broader adoption.

Purpose of the Study:

  • To develop a method for completing sparse LiDAR intensity maps by leveraging correlated depth information.
  • To enable the effective application of mature computer vision algorithms to LiDAR data without modification.
  • To enhance the robustness and applicability of LiDAR sensors in diverse environmental conditions.

Main Methods:

  • Proposing LiDAR-Net, an end-to-end convolutional neural network designed for joint completion of sparse intensity and depth measurements.
  • Exploiting the inherent correlations between LiDAR depth and intensity data within the neural network architecture.
  • Implementing an intensity fusion technique to generate high-quality ground truth data for network training.

Main Results:

  • Demonstrated that fusing intensity and depth data significantly benefits the task of LiDAR intensity map completion.
  • Achieved improved performance in completing sparse LiDAR intensity maps through the proposed joint learning approach.
  • Successfully applied an off-the-shelf lane segmentation algorithm to the completed intensity maps, showing robust performance under varying illumination.

Conclusions:

  • The proposed intensity completion method effectively leverages LiDAR depth and intensity correlations for enhanced data representation.
  • Completed LiDAR intensity maps facilitate the use of standard computer vision algorithms, expanding LiDAR's practical applications.
  • This approach enhances LiDAR's reliability and performance, particularly in scenarios where ambient illumination poses challenges for traditional sensors.