Underwater Robot Localization Research Articles

A Review of Underwater Robot Localization in Confined Spaces

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in confined underwater environments, researchers have studied different underwater positioning methods and have selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have developed some bio-inspired autonomous positioning methods. This article introduces applicable robot positioning methods and sensors in confined underwater environments and discusses the research directions of robot positioning methods in such environments.

Tightly-Coupled Visual-Inertial-Pressure Fusion Using Forward and Backward IMU Preintegration

In this work, we present a visual-inertial-pressure (VIP) fusion method for underwater robot localization. Specifically, this letter focuses on the tightly-coupled fusion of pressure measurements into a visual inertial odometry (VIO) based on sliding window optimization. Previous works used to associate partial pressure measurements with the nearest keyframes, which not only fail to utilize all the pressure measurement information but also introduce measurement errors that only lead to sub-optimal solutions. Inspired by the current tightly-coupled visual-inertial-GPS and visual-inertial-UWB fusion methods, this letter uses IMU preintegration algorithm to derive the pressure factors. Furthermore, we propose a backward IMU preintegration method and the pressure factors are derived using forward or backward IMU preintegration based on the time-offset between the pressure measurements and the adjacent keyframes. Quantitative and qualitative analyses through simulation and real-world datasets experiments demonstrate the effectiveness of the method with negligible time cost.

Neural network for black-box fusion of underwater robot localization under unmodeled noise

The research on autonomous robotics has focused on the aspect of information fusion from redundant estimates. Choosing a convenient fusion policy, that reduces the impact of unmodeled noise, and is computationally efficient, is an open research issue. The objective of this work is to study the problem of underwater localization which is a challenging field of research, given the dynamic aspect of the environment. For this, we explore navigation task scenarios based on inertial and geophysical sensory. We propose a neural network framework named B-PR-F which heuristically performs adaptable fusion of information, based on the principle of contextual anticipation of the localization signal within an ordered processing neighborhood. In the framework black-box unimodal estimations are related to the task context, and the confidence on individual estimates is evaluated before fusing information. A study conducted in a virtual environment illustrates the relevance of the model in fusing information under multiple task scenarios. A real experiment shows that our model outperforms the Kalman Filter and the Augmented Monte Carlo Localization algorithms in the task. We believe that the principle proposed can be relevant to related application fields, involving the problem of state estimation from the fusion of redundant information.

Synchronous and Asynchronous Application of a Filtering Method for Underwater Robot Localization

This paper reports a method that fuses multiple sensor measurements for location estimation of an underwater robot. Synchronous and asynchronous (AS) implementation of the method are also proposed. Extended Kalman filter (EKF) is used to fuse four types of measurements: linear velocity by Doppler velocity log (DVL), angular velocity by gyroscope, ranges to acoustic beacons, and depth. The EKF approach is implemented in three ways to deal with asynchrony in measurements in correction step. The three implementation methods are synchronous collective (SC), synchronous individual (SI), and AS application. These methods are verified and compared through simulation and test tank experiments. The test reveals that the application methods need to be selected depending on the measurement properties: dependency between the measurements and degree of asynchrony. The distinctive features proposed in this study are three application methods together with derivation of an EKF approach to sensor fusion for underwater navigation.