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Robust and accurate estimation of position and attitude of a UUV (Unmanned Underwater Vehicle) from sonar scans is essential for simultaneous localization and mapping (SLAM). Both dead-reckoning based on the inertial navigation system and the motion parameter estimation based on the registration of the ultrasound scan sequence can contribute to the performance of the system. However, the rapidly-growing accumulated error tends to counteract the precise localization of the vehicle. In this paper, a method for loop-closure detection is proposed that adjusts the accumulated error for the underwater acoustic SLAM when the vehicle scans the underwater environment using an Mechanical Scanning Imaging Sonar (MSIS). Firstly, a similarity matrix for pairs of scans is constructed to represent the loop-closing tracks. In the registration step, two novel features, namely the intensity projection histograms and a polar gradient matrix, are extracted to calculate the translational and rotational parameters respectively. Secondly, the probability hypothesis density (PHD) filter is used to extract the possible loop-closure constraints from the similarity matrix, removing the random noise brought by accidental correlation and refining the concurrent loop-closing tracks resulted from long-range scanning. Lastly, the loop-closure constraints from the refined loop-closing tracks are fed into the GraphSLAM system to adjust the pose of each scan by constraint optimization. Experiments on the MSIS sonar images collected in structured and unstructured underwater environments validate the effectiveness of the proposed loop-closure detection method.

Underwater automatic target recognition (ATR) is a challenging task for marine robots due to the complex environment. The existing recognition methods basically use hand-crafted features and classifiers to recognize targets, which are difficult to achieve ideal recognition accuracy. In this paper, we proposed a novel method to realize accurate multiclass underwater ATR by using forward-looking sonar—Echoscope and deep convolutional neural networks (DCNNs). A complete recognition process from data preprocessing to network training and image recognition was realized. Firstly, we established a real, measured Echoscope sonar image dataset. Inspired by the human visual attention mechanism, the suspected target region was extracted via the graph-based manifold ranking method in image preprocessing. Secondly, an end-to-end DCNNs model, named EchoNet, was designed for Echoscope sonar image feature extraction and recognition. Finally, a network training method based on transfer learning was developed to solve the problem of insufficient training data, and mini-batch gradient descent was used for network optimization. Experimental results demonstrated that our method can implement efficiently, and the recognition accuracy on a nine-class underwater ATR task reached 97.3%, outperforming traditional feature-based methods. The proposed method is expected to be a potential novel technology for the intelligent perception of autonomous underwater vehicles.

A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV&rsquo, s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.

In view of a dynamic obstacle environment with motion uncertainty, we present a dynamic collision avoidance method based on the collision risk assessment and improved velocity obstacle method. First, through the fusion optimization of forward-looking sonar data, the redundancy of the data is reduced and the position, size and velocity information of the obstacles are obtained, which can provide an accurate decision-making basis for next-step collision avoidance. Second, according to minimum meeting time and the minimum distance between the obstacle and unmanned underwater vehicle (UUV), this paper establishes the collision risk assessment model, and screens key obstacles to avoid collision. Finally, the optimization objective function is established based on the improved velocity obstacle method, and a UUV motion characteristic is used to calculate the reachable velocity sets. The optimal collision speed of UUV is searched in velocity space. The corresponding heading and speed commands are calculated, and outputted to the motion control module. The above is the complete dynamic obstacle avoidance process. The simulation results show that the proposed method can obtain a better collision avoidance effect in the dynamic environment, and has good adaptability to the unknown dynamic environment.

This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 m at approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research. QC 20130822

Monitoring the field of operation of an underwater vehicle is crucial during missions near the sea floor. The forward-looking sonar is often the only available sensor for the observation of the ambient turbid water environment. Sonar image registration is not only a first step towards a panoramic mosaic but it also provides an initial motion parameter estimation for the vehicle self-localization and navigation. In this article, a peripheral mutual information (PMI) maximization method is proposed for the sonar image registration. Peripheral mutual information is inspired by regional mutual information (RMI) which makes use of the closed-form solution for the Shannon entropy by the assumption that the data vectors made of neighbouring pixels are normally distributed, an assumption that ignores correlations between the pixels in sonar images. To accommodate the fact that the neighbouring pixels show dependencies due to acoustic reverberation and dispersion, only the peripheral information in the neighbourhood of a pixel is used in peripheral mutual information for the calculation of the mutual information. Experiments show that the peripheral mutual information registration function is much smoother than that of regional mutual information. Further experiments on the two-dimensional forward-looking sonar image registration demonstrate the efficiency of peripheral mutual information.

Vehicle operations in underwater environments are frequently compromised by poor visibility conditions. In this thesis, we propose an end-to-end mosaicing framework tailored to the characteristics of forward-looking sonar imagery in order to build consistent overviews of planar underwater areas regardless of water visibility. Our solution targets versatility: it enables the generation of acoustic mosaics that involve roto-translational motions and comprise different vehicle tracklines, it is suitable for a wide range of scenarios, it can be applicable on data collected with minimally instrumented vehicles, and it allows both offline and real-time operation. To this end, we address the key problem of image registration on forward-looking sonar images and we provide solutions for the global alignment of the sonar frames along the trajectory as well as their blending into a smooth acoustic mosaic. To validate the full proposed framework, an extensive experimental section is reported showing successful results in relevant field applications., Freqüentment, les operacions amb vehicles en entorns submarins estan condicionades a la visibilitat de l’aigua. En aquesta tesi, proposem un sistema complet per a la construcció de mapes subaquàtics adaptat a les característiques dels sonars de visió frontal, per tal de construir mosaics d’imatges acústiques independentment de la visibilitat de l’aigua. La solució proposada permet la generació de mapes que involucren moviments roto-translacionals i múltiples transectes, és adequada per un ampli ventall d’escenaris i permet la generació de mosaics tant en mode post-processat com en temps real. Proposem un nou mètode per al registre d'imatges d'aquest tipus de sonar, així com també solucions per a dur a terme l'alineament global de les imatges al llarg d'una trajectòria i per a fusionar les imatges en un mosaic acústic. Per tal de validar el funcionament de tot el sistema es presenten experiments demonstrant resultats satisfactòris en el marc d'aplicacions reals.

Sound backscattered from the seabed has been measured in a 10 × 10-nmi2 region of the North Sea, characterized by a variety of bottom types, including mud, sand, and gravel. The backscattering strength measurements are made by a forward-looking sonar, operating at 100 kHz and tilted at an angle of 30° from the horizontal. Fifty bottom-grab samples, representative of the uppermost 20 cm, were taken and analyzed for gravel content, shell content, and grain-size distribution. The backscatter measurements are correlated with the gravel percentage, shell percentage, and median grain size. A strong positive correlation of the measured backscatter strength with shell and gravel percentage is observed. Additionally, a small positive correlation between the backscatter strength and the median grain size of the sand and mud part exists. The derived values for the backscatter strength compare well to those presented in the recent literature. From a least squares curve-fitting analysis, an empirical expression is obtained giving backscatter strength as a function of gravel percentage, shell percentage, and the median grain size of the sand and mud part. © 2007 IEEE.

Sound backscattered from the seabed has been measured in a 10 × 10-nmi2 region of the North Sea, characterized by a variety of bottom types, including mud, sand, and gravel. The backscattering strength measurements are made by a forward-looking sonar, operating at 100 kHz and tilted at an angle of 30° from the horizontal. Fifty bottom-grab samples, representative of the uppermost 20 cm, were taken and analyzed for gravel content, shell content, and grain-size distribution. The backscatter measurements are correlated with the gravel percentage, shell percentage, and median grain size. A strong positive correlation of the measured backscatter strength with shell and gravel percentage is observed. Additionally, a small positive correlation between the backscatter strength and the median grain size of the sand and mud part exists. The derived values for the backscatter strength compare well to those presented in the recent literature. From a least squares curve-fitting analysis, an empirical expression is obtained giving backscatter strength as a function of gravel percentage, shell percentage, and the median grain size of the sand and mud part. © 2007 IEEE.