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

Autor:	Hakyoung Chung, Haeyong Yang, Jang Gyu Lee, KyuCheol Park
Rok vydání:	1998
Předmět:	Engineering business.industry Estimator Mobile robot Kalman filter Sensor fusion Sonar Computer Science::Robotics Extended Kalman filter Control theory Position (vector) Computer vision Artificial intelligence business Encoder
Zdroj:	IFAC Proceedings Volumes. 31:183-187
ISSN:	1474-6670
DOI:	10.1016/s1474-6670(17)44082-1
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::38b9e95dc68bfe167f6806c3debadba7 https://doi.org/10.1016/s1474-6670(17)44082-1 Zobrazit plný text záznamu