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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Traversable Region Detection and Tracking for a Sparse 3D Laser Scanner for Off-Road Environments Using Range Images.

Sensors (Basel, Switzerland)·2023
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Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments.

Nahyeong Kim1, Jhonghyun An1

  • 1School of Computing, Gachon University, Seongnam-si 1332, Republic of Korea.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary

We developed a knowledge distillation (KD) method for efficient off-road environment segmentation using LiDAR range images. This approach balances accuracy (mIoU) and computational cost (GFLOPS) for safer autonomous driving.

Keywords:
LiDAR point cloudknowledge distillationoff-roadpoint cloud projectionrange imageself-driving

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Off-road autonomous systems require computationally efficient perception due to irregular terrains and hardware risks.
  • Processing detailed 3D LiDAR point clouds demands significant computational resources.
  • Existing methods may not adequately balance performance and efficiency for real-time off-road navigation.

Purpose of the Study:

  • To propose a knowledge distillation (KD) method for segmenting off-road environment range images.
  • To enable computationally efficient off-road self-driving systems.
  • To achieve a favorable trade-off between segmentation accuracy and computational cost.

Main Methods:

  • Utilized LiDAR point cloud range images, converting 3D data to 2D images via projection with depth information.
  • Employed a soft label-based knowledge distillation (SLKD) technique to transfer knowledge from a large teacher network to a lightweight student network.
  • Evaluated the SLKD method on the RELLIS-3D off-road dataset, measuring mean intersection over union (mIoU) and GFLOPS.

Main Results:

  • The SLKD method demonstrated effective knowledge transfer from a teacher to a student network.
  • Achieved a favorable balance between segmentation accuracy (mIoU) and computational efficiency (GFLOPS).
  • The lightweight student network, trained with SLKD, offers reduced computational requirements.

Conclusions:

  • The proposed SLKD method provides an efficient solution for off-road environment segmentation.
  • This approach shows significant promise for developing cost-effective and efficient off-road autonomous driving systems.
  • Reduced computational costs through SLKD can enhance the feasibility of deploying autonomous systems in challenging terrains.