• 한국군사과학기술학회지
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• 제16권2호
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• pp.95-100
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• 2013

Localization is very important for the autonomous navigation of Unmanned Ground Vehicles; however, it is difficult that they have a precise Inertial Navigation System(INS) sensor, especially Small Unmanned Ground Vehicle(SUGV). Moreover, there are some condition such as denial of global position system(GPS), GPS/INS integrated system is not robust. This paper proposes the estimation algorithm with optical flow sensor and INS. Being compared with previous researches, the proposed algorithm is suitable for skid steering vehicles. We revised the measurement model of previous research for the accuracy of side direction position. Experimental results were performed to verify the algorithm, and the result showed an excellent performance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 추계학술대회 논문집
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• pp.511-512
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• 2008

• 한국인터넷방송통신학회논문지
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• 제19권4호
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• pp.71-76
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• 2019

스마트폰을 이용한 실내 위치 추적 기술에 관한 연구가 활발하게 진행되고 있다. 특히 스마트폰의 이동 거리를 정확하게 측정하여 사용자의 이동 경로를 지도에 표시하여야 한다. 스마트폰에 탑재 되어있는 센서를 이용한 위치 추적 기술은 오래 전부터 사용 되어 왔으나, 센서만 이용하여 사용자의 이동 거리를 측정하기에는 정확하지 못하다. 따라서 사용자가 스마트폰을 일정한 자세로 들고 이동할 때, 이동 거리를 정확하게 측정할 수 있는 적절한 알고리즘을 연구 개발해야 한다. 본 논문에서는 피라미드 기반 광류 추정 방법에서 스마트폰의 화면을 제한하여 사용자의 발모양을 제거함으로써 이동거리 추정 오차를 줄이기 위한 방법을 제안하고, 실험으로 그 성능을 검증하였다.

• 제어로봇시스템학회논문지
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• 제18권1호
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• pp.54-61
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• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.