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Two-bounce LiDAR Digital Twin based data generation framework for object detection in the blind spot

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(6), pp.1~10
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 7, 2025
  • Accepted : May 29, 2025
  • Published : June 30, 2025

Jae-Hun Hwang 1 Seung Yeop Ha 2 Jun-Seok Yun 2 Min Su Kim 2 Sanga Lee 3 Jong Pil Yun 2 Hong-In Won 2

1한국생산기술연구원/한양대학교
2한국생산기술연구원
3한국생산기술연구원(인천)

Accredited

ABSTRACT

Obstacle detection in blind spots has emerged as a critical challenge due to the increasing adoption of Automated Guided Vehicle (AGV) and Autonomous Mobile Robot (AMR) systems in modern industrial environments. While conventional ultrasonic and single-bounce LiDAR sensors exhibit limitations in complex environments, Two-Bounce LiDAR offers higher detection precision but faces challenges such as high cost and limited diversity in training data. To address these challenge, this study proposes a framework that combines Two-Bounce LiDAR with Digital Twin simulation to generate synthetic data under various scenarios. Object detection models trained with this data achieved an average detection accuracy of 92.5% in environments including blind spots, with an average improvement of 14.67 percentage points in accuracy and 15.69 points in F1-score compared to models trained only on normal-environment data. These findings demonstrate that the proposed framework effectively overcomes data collection limitations and significantly enhances the safety and reliability of AGV and AMR operations.

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