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Related Experiment Video

Updated: Sep 18, 2025

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Metric scale non-fixed obstacles distance estimation using a 3D map and a monocular camera.

Daijiro Higashi1, Naoki Fukuta1, Tsuyoshi Tasaki1

  • 1Graduate School of Science and Technology, Meijo University, Nagoya, Japan.

Frontiers in Robotics and AI
|June 27, 2025
PubMed
Summary

This study introduces DifSeg, a novel loss function to improve metric scale obstacle detection for autonomous driving. DifSeg significantly enhances distance estimation accuracy for non-fixed obstacles using monocular cameras.

Keywords:
3D mapautonomous drivingdepth completionmonocular depth estimationobstacle detectionsemantic segmentation

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Obstacle avoidance is critical for autonomous driving safety.
  • Metric scale obstacle detection using monocular cameras is an active research area.
  • Existing methods like PMOD-Net struggle with accurate distance estimation for dynamic, non-mapped obstacles.

Purpose of the Study:

  • To improve the distance estimation accuracy of non-fixed obstacles for autonomous driving.
  • To enhance the performance of metric scale obstacle detection systems using monocular cameras.

Main Methods:

  • Developed a new loss function, DifSeg, specifically for improving distance estimation of non-fixed obstacles.
  • DifSeg leverages object detection results to focus training on non-fixed obstacle regions.
  • Integrated DifSeg into PMOD-Net, a system for metric scale obstacle detection using monocular cameras and 3D maps.

Main Results:

  • DifSeg significantly improved distance estimation accuracy for non-fixed obstacles across multiple datasets (CARLA, KITTI, indoor).
  • On the KITTI dataset, the proposed method achieved a distance error of 2.42 m, outperforming the latest monocular depth estimation methods by 2.14 m.
  • The method demonstrated enhanced performance in real-world and simulated autonomous driving scenarios.

Conclusions:

  • The DifSeg loss function is effective in enhancing metric scale obstacle detection for autonomous driving.
  • This approach offers a promising solution for improving the safety and reliability of autonomous vehicles, particularly in dynamic environments.
  • Further research can explore the integration of DifSeg with other sensor modalities for even more robust obstacle detection.