Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using
Looking Forward Sonar

doi:10.1007/s12204-012-1234-8

上海交通大学学报（英文版） ›› 2012, Vol. 17 ›› Issue (1): 91-097.doi: 10.1007/s12204-012-1234-8

Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar

ZENG Wen-jing (曾文静), WAN Lei (万磊), ZHANG Tie-dong (张铁栋), HUANG Shu-ling (黄蜀玲)

(State Key Laboratory of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin 150001, China)

收稿日期:2011-01-04 出版日期:2012-02-29 发布日期:2012-03-21
通讯作者: ZENG Wen-jing (曾文静), E-mail:zenwenjing@163.com

Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar

ZENG Wen-jing (曾文静), WAN Lei (万磊), ZHANG Tie-dong (张铁栋), HUANG Shu-ling (黄蜀玲)

(State Key Laboratory of Autonomous Underwater Vehicle, Harbin Engineering University, Harbin 150001, China)

Received:2011-01-04 Online:2012-02-29 Published:2012-03-21
Contact: ZENG Wen-jing (曾文静), E-mail:zenwenjing@163.com

摘要/Abstract

摘要： Abstract: A method of underwater simultaneous localization and mapping (SLAM) based on on-board looking forward sonar is proposed. The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM. Extended Kalman filter (EKF) is selected as the kernel approach to enable the underwater vehicle to construct a feature map, and the EKF can locate the underwater vehicle through the map. In order to improve the association efficiency, a novel association method based on ant colony algorithm is introduced. Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed. The proposed method maintains better association efficiency and reduces navigation error, and is effective and feasible.

关键词: simultaneous localization and mapping (SLAM), autonomous underwater vehicle (AUV), looking forward sonar, extended Kalman filter (EKF)

Abstract: Abstract: A method of underwater simultaneous localization and mapping (SLAM) based on on-board looking forward sonar is proposed. The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM. Extended Kalman filter (EKF) is selected as the kernel approach to enable the underwater vehicle to construct a feature map, and the EKF can locate the underwater vehicle through the map. In order to improve the association efficiency, a novel association method based on ant colony algorithm is introduced. Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed. The proposed method maintains better association efficiency and reduces navigation error, and is effective and feasible.

Key words: simultaneous localization and mapping (SLAM), autonomous underwater vehicle (AUV), looking forward sonar, extended Kalman filter (EKF)

中图分类号:

U674

ZENG Wen-jing (曾文静), WAN Lei (万磊), ZHANG Tie-dong (张铁栋), HUANG . Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar[J]. 上海交通大学学报（英文版）, 2012, 17(1): 91-097.

ZENG Wen-jing (曾文静), WAN Lei (万磊), ZHANG Tie-dong (张铁栋), HUANG Shu-ling (黄蜀玲). Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(1): 91-097.

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Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar

Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar

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