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

Bo He,Chao Li,Tianhong Yan,Shujing Zhang,Hongjin Zhang,Yan Liang

doi:10.3390/s111110958

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.

Highlights

Autonomous Underwater Vehicles (AUVs) have portable energy and self-control ability which make them different from remote operate vehicles (ROVs)
Google Earth) and AUV trajectory measured by GPS are shown in Figure 9, where a starting point with direction is marked using a green arrow
There are two reasons for this: on the one hand, error accumulation caused by nonlinear model linearization will result in inconsistency, and this problem is the same as the EKF algorithm and exists in most of the simultaneous localization and mapping (SLAM) algorithms based on the Gaussian linear filters; On the other hand, the sparsification, which marginalized out the weak links, could affect consistency though it has been proved that Sparse Extended Information Filter (SEIF)-SLAM has relatively good consistency [26,28]

Summary

Introduction

Autonomous Underwater Vehicles (AUVs) have portable energy and self-control ability which make them different from remote operate vehicles (ROVs). They are suitable for commercial and military tasks underwater, under-ice or in other environments [1,2]. Inertial navigation systems (INS) are widely used in AUVs, but the navigation errors accumulate over time and waves and currents exacerbate this. Though the errors can be reduced periodically by using GPS, electromagnetic signals decay very quickly in the water, so the navigation of underwater vehicles cannot rely on GPS. Deployment and recovery of the baseline is time-consuming and expensive, which limits acoustic navigation in large-scale environments [5]. For all the reasons mentioned above, autonomous underwater navigation is considered one of the most challenging issues for AUVs [6]

Results

Discussion

Conclusion