Abstract
Database-referenced navigation (DBRN) using geophysical information is often implemented on autonomous underwater vehicles (AUVs) to correct the positional errors of the inertial navigation system (INS). The matching algorithm is a pivotal technique in DBRN. However, it is impossible to completely eliminate mismatches in practical application. Therefore, it is necessary to perform a mismatch detection method on the outputs of DBRN. In this paper, we propose a real-time triple constraint mismatch detection method. The proposed detection method is divided into three modules: the model fitting detection module, the spatial structure detection module, and the distance ratio detection module. In the model fitting detection module, the navigation characteristics of AUVs are used to select the fitting model. In the spatial structure detection module, the proposed method performs the mismatch detection based on the affine transformation relationship between the INS-indicated trajectory and the corresponding matched trajectory. In the distance ratio detection module, we derive the distance ratio constraint between the INS-indicated trajectory and the corresponding matched trajectory. Simulations based on an actual geomagnetic anomaly base map have been performed for the validation of the proposed method.
Highlights
Autonomous underwater vehicles (AUVs) are widely used in a variety of tasks, including oceanographic surveys, bathymetric data collection in marine and riverine environments, patrol and reconnaissance missions in the military field, and rescue duties [1]
In order to solve the problem of a single detection mode and poor real-time performance, we suggest a real-time triple constraint mismatch detection method
In view of the limitations of M-estimator sample consequence (MSAC) and weighted graph transformation matching (WGTM) applied on AUV, this paper proposes a real-time triple constraint mismatch detection method
Summary
Autonomous underwater vehicles (AUVs) are widely used in a variety of tasks, including oceanographic surveys, bathymetric data collection in marine and riverine environments, patrol and reconnaissance missions in the military field, and rescue duties [1]. TRN obtains a range measurements by using sonar sensors installed on a vehicle These measurements are matched with a priori digital map of the terrain elevation, to estimate the vehicle position. Random sample consensus (RANSAC)-based algorithms [21,22,23] and graph transformation matching (GTM)-based algorithms [24,25] are two types of commonly used mismatch detection methods in the field of feature points based image registration. The proposed method, considering the model fitting constraint, spatial structure constraint, and distance ratio constraint, is presented to deal with mismatched point detection problem in matching algorithms-based DBRN systems for AUVs. In model-fitting detection module, the proposed method eliminates points that deviate significantly from the matched trajectory.
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