A Rotating Sonar and a Differential Encoder Data Fusion for Map-Based Dynamic Localization

Abstract In this paper, a dynamic localization method using a rotating sonar and a map is proposed. The proposed method is implemented by using an extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while a mobile robot is moving continuously. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error.