Localization In Underwater Environments Research Articles

YOLO-Based 3D Perception for UVMS Grasping

This study develops a YOLO (You Only Look Once)-based 3D perception algorithm for UVMS (Underwater Vehicle-Manipulator Systems) for precise object detection and localization, crucial for enhanced grasping tasks. The object detection algorithm, YOLOv5s-CS, integrates an enhanced YOLOv5s model with C3SE attention and SPPFCSPC feature fusion, optimized for precise detection and two-dimensional localization in underwater environments with sparse features. Distance measurement is further improved by refining the SGBM (Semi-Global Block Matching) algorithm with Census transform and subpixel interpolation. Ablation studies highlight the YOLOv5s-CS model’s enhanced performance, with a 3.5% increase in mAP and a 6.4% rise in F1 score over the base YOLOv5s, and a 2.1% mAP improvement with 15% faster execution than YOLOv8s. Implemented on a UVMS, the algorithm successfully conducted pool grasping experiments, proving its applicability for autonomous underwater robotics.

Efficient and Accurate Target Localization in Underwater Environment

Localization is the basic feature of wireless sensor networks (WSN) and estimating the time difference of arrival (TDoA), and angle of arrival (AoA) is the most used schemes for localization. Underwater wireless sensor network (UWSN) is extensively used for data collection in an underwater environment for both military and civilian applications. Nevertheless, efficient and accurate localization algorithms are essential for the UWSN because of the dynamical property of the underwater surrounding. Also, data management, collection, and processing for WSNs have become a more active topic nowadays in computer science, such as database system and data mining. The objective of deploying the WSNs applications is to collect the real-time data, which has very challenging due to the capacity of communication and high data generated by WSNs. For this purpose, the time and location are the basic aspects when a sensor collects data, especially for the case of location-aware data. Many researchers have studied the underwater sensor nodes localization and they have considered the sensor location where the data is collected and most of them focused on the fixed sensor nodes. In this research work, energy efficient and accurate localization schemes are presented named as distance-based and angle-based schemes for the underwater environment with relatively less energy consumption and mean estimation errors (MEEs). The proposed schemes mainly focus on the localization of underwater nodes and especially on the MEEs in localization. The extensive simulation is performed to compare the proposed schemes with other counterpart schemes. The results show that the proposed schemes outperform other counterpart schemes in terms of MEEs of localization and energy consumption.

Game-Theoretic Topology Controlfor Opportunistic Localization in Sparse Underwater Sensor Networks

In this paper, we propose a localization scheme named Opportunistic Localization by Topology Control (OLTC), specifically for sparse Underwater Sensor Networks (UWSNs). In a UWSN, an unlocalized sensor node finds its location by utilizing the spatio-temporal relation with the reference nodes. Generally, UWSNs are sparsely deployed because of the high implementation cost, and unfortunately, the network topology experiences partitioning due to the effect of passive node mobility. Consequently, most of the underwater sensor nodes lack the required number of reference nodes for localization in underwater environments. The existing literature is deficient in addressing the problem of node localization in the above mentioned scenario. Antagonistically, however, we promote that even in such sparse UWSN context, it is possible to localize the nodes by exploiting their available opportunities. We formulate a game-theoretic model based on the Single-Leader-Multi-Follower Stackelberg game for topology control of the unlocalized and localized nodes. We also prove that both the players choose strategies to reach a socially optimal Stackelberg-Nash-Cournot Equilibrium . NS-3 based simulation results indicate that the localization coverage of the network increases upto 1.5 times compared to the existing state-of-the-art. The energy-efficiency of OLTC has also been established.

Object Recognition and Localization: The Role of Tactile Sensors

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

AUV Navigation and Localization: A Review

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

SLSMP: Time Synchronization and Localization Using Seawater Movement Pattern in Underwater Wireless Networks

Time synchronization and localization in underwater environment are challenging due to high propagation delay, time measurement error, and node mobility. Although synchronization and localization depend on each other and have the similar process, they have been usually handled separately. In this paper, we suggest time synchronization and localization based on the semiperiodic property of seawater movement, called SLSMP. Firstly, we analyze error factors in time synchronization and localization and then propose a method to handle those errors. For more accurate synchronization, SLSMP controls the transmission instant by exploiting the pattern of seawater movement and node deployment. Then SLSMP progressively decreases the localization errors by applying the Kalman filter or averaging filter. Finally, INS (inertial navigation system) is adopted to relieve localization error caused by node mobility and error propagation problem. The simulation results show that SLSMP reduces time synchronization error by 2.5 ms and 0.56 ms compared with TSHL and MU-Sync, respectively. Also localization error is lessened by 44.73% compared with the single multilateration.

Development of an underwater binaural head model

The human brain has difficulty localizing sound in aquatic environments, where the acoustical properties of water greatly impede the mechanisms by which the brain interprets binaural signals. In this experiment, a hollow steel sphere with antipodal hydrophones is exposed to noise bursts in an underwater environment. The sphere can be filled with various materials to alter the apparatus’ rigid qualities. Interaural time and level differences (ITDs and ILDs, respectively) are calculated from recordings of these noises and compared to a theoretical model for sound propagation around a non-rigid head in an effort to better characterize binaural hearing in underwater surroundings. While the theoretical behavior of sound diffracting around a rigid head has been well documented, the similar problem involving a flexible head has largely been left to experimental methods due to the computational complexity of the task. In the current study, a new computational model, capable of predicting ITDs and ILDs for sounds encountering a non-rigid sphere in diverse environments, is used. Both the model and the experiment will be introduced in this presentation. The findings have significant implications for the future development of reliable methods for improving sound localization in underwater environments, for instance for recreational divers.

Blind deconvolution for robust signal estimation and approximate source localization

Synthetic time reversal (STR) is a technique for blind deconvolution in an unknown multipath environment that relies on generic features (rays or modes) of multipath sound propagation. This paper describes how ray-based STR signal estimates may be improved and how ray-based STR sound-channel impulse-response estimates may be exploited for approximate source localization in underwater environments. Findings are based on simulations and underwater experiments involving source-array ranges from 100 m to 1 km in 60 -m-deep water and chirp signals with a bandwidth of 1.5-4.0 kHz. Signal estimation performance is quantified by the correlation coefficient between the source-broadcast and the STR-estimated signals for a variable number N of array elements, 2 ≤ N ≤ 32, and a range of signal-to-noise ratio (SNR), -5 dB ≤ SNR ≤ 30 dB. At high SNR, STR-estimated signals are found to have cross-correlation coefficients of ∼90% with as few as four array elements, and similar performance may be achieved at a SNR of nearly 0 dB with 32 array elements. When the broadband STR-estimated impulse response is used for source localization via a simple ray-based backpropagation scheme, the results are less ambiguous than those obtained from conventional broadband matched field processing.

Silent Localization of Underwater Sensors Using Magnetometers

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

Multiple source localization using a maximum a posteriori Gibbs sampling approach

Multiple source localization in underwater environments is approached within a matched-field processing framework. A maximum a posteriori estimation method is proposed that estimates source location and spectral characteristics of multiple sources via Gibbs sampling. The method facilitates localization of weak sources which are typically masked by the presence of strong interferers. A performance evaluation study based on Monte Carlo simulations shows that the proposed maximum a posteriori estimation approach is superior to simple coherent matched-field interference cancellation. The proposed method is also tested on the estimation of the number of sources present, providing probability distributions in addition to point estimates for the number of sources.