무인선 탑재된 주변 탐색 센서의 융합 접근 방안

Abstract

Unmanned surface vehicle (USV) has sensor modules to obtain information about object and environment around USV which can disturb USVs path and be obstacles. Each sensor module operates independently and its outputs are released without interacting with other sensor modules. In this paper, approach of sensor fusion method is briefly investigated for obtaining information about obstacles in USV with sensor modules. The prevalent methods for sensor fusion are weighted average, Kalman filtering, Bayesian inference, and Dempster-Shafer method, which are briefly compared in the sense of how to organize sensor fusion scheme for USV.ut interacting with other sensor modules. In this paper, approach of sensor fusion method is briefly investigated for obtaining information about obstacles in USV with sensor modules. The prevalent methods for sensor fusion are weighted average, Kalman filtering, Bayesian inference, and Dempster-Shafer method, which are briefly compared in the sense of how to organize sensor fusion scheme for USV.