Visual Sensing of the Light Spot of a Laser Pointer for Robotic Applications
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, we present visual sensing techniques that can be used to teach a robot using a laser pointer. The light spot of an off-the-shelf laser pointer is detected and its movement is tracked on consecutive images of a camera. The three-dimen- sional position of the spot is calculated using stereo cameras. The light spot on the image is detected based on its color, brightness, and shape. The detection results in a binary image, and morphological processing steps are performed on the image to refine the detection. The movement of the laser spot is measured using two methods. The first is a simple method of specifying the region of interest (ROI) centered at the current location of the light spot and finding the spot within the ROI on the next image. It is assumed that the movement of the spot is not large on two consecutive images. The second method is using a Kalman filter, which has been widely employed in trajectory estimation problems. In our simulation study of various cases, Kalman filtering shows better results mostly. However, there is a problem of fitting the system model of the filter to the pattern of the spot movement.
Abstract
In this paper, we present visual sensing techniques that can be used to teach a robot using a laser pointer. The light spot of an off-the-shelf laser pointer is detected and its movement is tracked on consecutive images of a camera. The three-dimen- sional position of the spot is calculated using stereo cameras. The light spot on the image is detected based on its color, brightness, and shape. The detection results in a binary image, and morphological processing steps are performed on the image to refine the detection. The movement of the laser spot is measured using two methods. The first is a simple method of specifying the region of interest (ROI) centered at the current location of the light spot and finding the spot within the ROI on the next image. It is assumed that the movement of the spot is not large on two consecutive images. The second method is using a Kalman filter, which has been widely employed in trajectory estimation problems. In our simulation study of various cases, Kalman filtering shows better results mostly. However, there is a problem of fitting the system model of the filter to the pattern of the spot movement.
참고문헌 (10)
- H. R. Everett, "Sensors for Mobile Robots: Theory and Application" (1995), A. K. Peters, Ltd.
- Y. Zhang, "Object detection and recognition of intelligent service robot based on deep learning", Proc. IEEE Conf. Cybern. Intell. Syst. IEEE Conf. Robot. Autom. Mechatron., pp.171~176 (2017)
- D. U. Kim, "Vision-based automatic detection of a laser spotlight on a projector screen", Proc. Int. Conf. Comput. Commun., Syst. (2015)
- P. Johansson, "Robot Cell Calibration Using a Laser Pointer SME Robot", pp.2007
- J.-H. Kim, "LAROB: Laser-guided underwater mobile robot for reactor vessel inspection", IEEE, 2014, pp.19(4), 1216~1225
- J. N. Pires, "Robot-by-voice: Experiments on commanding an industrial robot using the human voice", Ind. Robot, 32(6), pp.505~511 (2005)[DOI]
- P. Diwan, "Hand Gesture Controlled Robot", Artif. Intell. Syst. Mach. Learn., 6(5), pp.176~179 (2014)
- 도용태, "새로운 선형의 외형적 카메라 보정 기법", 센서학회지, 23(1), pp.66~71 (2014), 한국센서학회[DOI]