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. 2017 May 21;17(5):1174.
doi: 10.3390/s17051174.

AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

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Free PMC article

AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Xin Yuan et al. Sensors (Basel). .
Free PMC article

Abstract

In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.

Keywords: FastSLAM 2.0; augmented extended Kalman filter (AEKF); computational complexity; loop closure; underwater simultaneous localization and mapping (SLAM).

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The problem of robotic localization and mapping.
Figure 2
Figure 2
The topological map.
Figure 3
Figure 3
The landmark map.
Figure 4
Figure 4
A robot measuring relative observations to environmental landmarks.
Figure 5
Figure 5
The flowchart of the SLAM process.
Figure 6
Figure 6
The SLAM graphical model.
Figure 7
Figure 7
(a) Before closing the loop; (b) After closing the loop.
Figure 8
Figure 8
The AEKF estimator.
Figure 9
Figure 9
The flow chart of SLAM procedure based on an AEKF, modified in [7].
Figure 10
Figure 10
The architecture of the AEKF-SLAM-based robotic navigation system, as in [7].
Figure 11
Figure 11
The robot motion model.
Figure 12
Figure 12
The robot observation model.
Figure 13
Figure 13
(a) The robot is observing the landmarks A and B in the AEKF-SLAM dense loop map; (b) The robot is getting measurements A and B in the FastSLAM 2.0 dense loop map.
Figure 14
Figure 14
(a) Partial magnification of the AEKF-SLAM line map; (b) Partial magnification of the FastSLAM 2.0 line map.
Figure 15
Figure 15
The simulated SWARMs vehicles.
Figure 16
Figure 16
Link all the actors for landmark localization and seabed mapping in the SWARMs project.

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References

    1. Dissanayake G., Durrant-Whyte H., Bailey T. A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem; Proceedings of the 2000 IEEE International Conference on Robotics & Automation; San Francisco, CA, USA. 24–28 April 2000; pp. 1009–1014.
    1. Durrant-Whyte H., Majumder S., Thrun S., Battista M., Scheding S. A Bayesian algorithm for simultaneous localization and map building; Proceedings of the 10th International Symposium of Robotics Research (ISRR’01); Lorne, Australia. 9–11 November 2001.
    1. Hidalgo F., Bräunl T. Review of underwater SLAM techniques; Proceedings of the 6th International Conference on Automation, Robotics and Applications; Queenstown, The New Zealand. 17–19 February 2015; pp. 305–311.
    1. Bailey T., Durrant-Whyte H. Simultaneous localization and mapping (SLAM): Part II the state of the art. IEEE Robot. Autom. Mag. 2016;13:108–117. doi: 10.1109/MRA.2006.1678144. - DOI
    1. Thrun S. Simultaneous localization and mapping. In: Jefferies M.E., editor. Robotics and Cognitive Approaches to Spatial Mapping. Springer; Berlin/Heidelberg, Germany: 2008. pp. 13–41.
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