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이동로봇의 안전한 엘리베이터 탑승을 위한 RGB-D 센서 기반의 엘리베이터 인식 및 위치추정

Elevator Recognition and Position Estimation based on RGB-D Sensor for Safe Elevator Boarding

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초록

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

Abstract

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

참고문헌 (11)

  1. A. A. Abdulla, "Multi-floor navigation method for mobile robot transportation based on StarGazer sensors in life science automation", 2015 IEEE International Instrumentation and Measurement Technology Conference, pp.428~433 (2015)
  2. S. W. Ma, "Localization using Fiducial Markers and Elevator Door", M.S, pp.2012
  3. 강정관, "Recognition and Path Planning Strategy for Autonomous Navigation in the Elevator Environment", International Journal of Control, Automation, and Systems, 8(4), pp.808~821 (2010), 제어·로봇·시스템학회
  4. J. Y. Baek, "A study on detecting elevator entrance door using stereo vision in multi floor environment", 2009 ICCAS-SICE, pp.1370~1373 (2009)
  5. W. Boehler, "Investigating laser scanner accuracy", CIPA XIXth Int. Symposium, 34, pp.696~701 (2003)
  6. D. H. Lee, "RGB-D Sensor based Elevator Recognition and Getting on and off an Elevator for Safe", M.S, pp.2018
  7. 마승완, "LRF와 카메라를 이용한 강인한 엘리베이터 문 인식", 제어.로봇.시스템학회 논문지, 18(6), pp.601~607 (2012), 제어·로봇·시스템학회[DOI]
  8. C. Szegedy, "Rethinking the Inception Architecture for Computer Vision", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2818~2826 (2016)
  9. C. Szegedy, "Inception-v4, inception-resnet and the impact of residual connections on learning", arXiv:1602.07261, ~ (2016)
  10. A. Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks", Communications of the ACM (2017)
  11. R. O. Duda, "Use of the Hough transform to detect lines and curves in pictures", Communications of the ACM (1972)

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