Sensors 2011, 11(11), 10958-10980; doi:10.3390/s111110958
Article

Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing

1 School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China 2 School of Mechanical & Electrical Engineering, China Jiliang University, 258 Xueyuan Street, Xiasha High-Edu Park, Hangzhou 310018, China 3 State Key Lab of Digital Manufacturing and Equipments Technology, Huazhong University of Science and Technology, Luoyu Road, Wuhan 430074, China
* Authors to whom correspondence should be addressed.
Received: 8 October 2011; in revised form: 11 November 2011 / Accepted: 18 November 2011 / Published: 22 November 2011
(This article belongs to the Section Physical Sensors)
PDF Full-text Download PDF Full-Text [1109 KB, uploaded 22 November 2011 09:28 CET]
Abstract: This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM.
Keywords: : autonomous underwater vehicle (AUV); extended information filter; localization; navigation; sonar

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

He, B.; Zhang, H.; Li, C.; Zhang, S.; Liang, Y.; Yan, T. Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing. Sensors 2011, 11, 10958-10980.

AMA Style

He B, Zhang H, Li C, Zhang S, Liang Y, Yan T. Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing. Sensors. 2011; 11(11):10958-10980.

Chicago/Turabian Style

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong. 2011. "Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing." Sensors 11, no. 11: 10958-10980.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert