The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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A Low-Cost Lidar Sensor based Glass Feature Extraction Method for an Accurate Map Representation using Statistical Moments

통계적 모멘트를 이용한 정확한 환경 지도 표현을 위한 저가 라이다 센서 기반 유리 특징점 추출 기법

  • An, Ye Chan (School of Electronic Engineering, Kumoh National Institute of Technology) ;
  • Lee, Seung Hwan (School of Electronic Engineering, Kumoh National Institute of Technology)
  • Received : 2020.12.15
  • Accepted : 2021.02.18
  • Published : 2021.05.31
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Abstract

This study addresses a low-cost lidar sensor-based glass feature extraction method for an accurate map representation using statistical moments, i.e. the mean and variance. Since the low-cost lidar sensor produces range-only data without intensity and multi-echo data, there are some difficulties in detecting glass-like objects. In this study, a principle that an incidence angle of a ray emitted from the lidar with respect to a glass surface is close to zero degrees is concerned for glass detection. Besides, all sensor data are preprocessed and clustered, which is represented using statistical moments as glass feature candidates. Glass features are selected among the candidates according to several conditions based on the principle and geometric relation in the global coordinate system. The accumulated glass features are classified according to the distance, which is lastly represented on the map. Several experiments were conducted in glass environments. The results showed that the proposed method accurately extracted and represented glass windows using proper parameters. The parameters were empirically designed and carefully analyzed. In future work, we will implement and perform the conventional SLAM algorithms combined with our glass feature extraction method in glass environments.

Keywords

Acknowledgement

This research was supported by Kumoh National Institute of Technology (2018-104-005)

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