소나 영상을 이용한 확률적 물체 인식 및 수중 이동체의 위치 추정

Abstract

This paper proposes a probability-based object recognition and underwater localization algorithm with artificial landmarks. It is organized as follows 1) recognizing artificial objects 2) A look at EKF (Extended Kalman Filter) SLAM for localization. Acoustic images from imaging sonar are very unstable, for this reason we developed artificial landmarks that can easily be detected in noisy environments. We also designed a probability-based, recognition framework. In this way, the distance and bearing of the recognized artificial landmarks are acquired, allowing us to perform the localization for our underwater vehicle. And then, EKF-based localization algorithm is carried out the localization which produce a path of underwater robot and a location of landmarks. The proposed localization algorithm is verified by experiments in a basin.calization. Acoustic images from imaging sonar are very unstable, for this reason we developed artificial landmarks that can easily be detected in noisy environments. We also designed a probability-based, recognition framework. In this way, the distance and bearing of the recognized artificial landmarks are acquired, allowing us to perform the localization for our underwater vehicle. And then, EKF-based localization algorithm is carried out the localization which produce a path of underwater robot and a location of landmarks. The proposed localization algorithm is verified by experiments in a basin.