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Real-time depth completion based on LiDAR-stereo for autonomous driving.

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This study fuses LiDAR and stereo camera data for safer autonomous driving. Our real-time network enhances 3D perception, overcoming individual sensor limitations in various conditions.

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Sensor Fusion

Background:

  • Autonomous driving relies on accurate 3D environmental perception.
  • Binocular stereo vision depth estimation is sensitive to environmental factors and distance.
  • LiDAR point clouds offer penetration but are sparse compared to images.

Purpose of the Study:

  • To develop a real-time LiDAR-stereo fusion network for enhanced depth completion.
  • To improve the accuracy and reliability of 3D information for autonomous driving systems.
  • To address limitations of individual sensors through cross-sensor fusion.

Main Methods:

  • Proposed a real-time LiDAR-stereo depth completion network.
  • Utilized injection guidance for fusing point clouds and binocular images.
  • Employed a kernel-connected spatial propagation network for depth refinement.

Main Results:

  • Achieved effective real-time performance on the KITTI dataset.
  • Demonstrated successful fusion of LiDAR and stereo data for accurate depth estimation.
  • Validated the method's robustness in challenging conditions and against sensor defects using p-KITTI.

Conclusions:

  • LiDAR-stereo fusion effectively enhances 3D perception for autonomous driving.
  • The proposed network provides accurate dense 3D information in real-time.
  • The method successfully mitigates individual sensor weaknesses and environmental challenges.