Abstract
Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods.
Highlights
An autonomous underwater vehicles (AUVs) is an untethered underwater robot which can navigate itself in the complex undersea environment
It can be described as that a mobile robot is placed in an unknown environment and the robot incrementally builds a consistent map of this environment while simultaneously determining its location within this map [1]
The covariance matrix in the extended Kalman filter (EKF)-simultaneous localization and mapping (SLAM) contains the covariance between the robot and environment features [7], which need to be updated after each estimation and correction
Summary
An AUV is an untethered underwater robot which can navigate itself in the complex undersea environment. The covariance matrix in the EKF-SLAM contains the covariance between the robot and environment features [7], which need to be updated after each estimation and correction As a result, it achieves O(n2) (the number of environmental features) computational complexity. In the ACFR of University of Sydney, the Oberon AUV extracted the point features from the sonar image to construct the environmental map while it estimates the robot position using EKF-SLAM [23]. The main contribution of this paper lies in the development of a modified-FastSLAM algorithm, which can be used effectively to construct a point-feature map of the undersea environment and localize the AUV position simultaneously.
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