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This paper presents a system to provide augmented localization to an AUV equipped with a side scan sonar. Upon revisiting an area, from which side scan data had previously been collected, the system generates an estimate to bound the error in the AUV’s estimate. Localization is accomplished through the comparison of sonar images. Image comparison i...
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... the return intensities for each ping are plotted in a "waterfall" image which depicts the return intensities for each transducer, plotted against horizontal time axes. See Figure 1 for an example. In the centre of the waterfall image is the nadir, which represents an effective gap in coverage due to the angle at which the sonar's transducers are mounted (to the sides). ...
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... must be taken in interpreting a waterfall image such as that shown in Figure 1. Pixels in this image cannot be interpreted as having a real and consistent physical scale. ...
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... it can be seen that an optimal configuration in terms of MCC is in the location of the peak. The peak value of the MCC score is 0.3343 with a size threshold of 15 and an angle threshold of 20. Figure 11 shows the average localizer error plotted against size and angle thresholds. It can be seen that the region of lowest error correlates to the region of the highest MCC score. ...
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... more observations are made, we eventually begin to converge on the correct solution, but not before the test ends. Figure 15 plots the performance of all three parameter sets. We see that (30,30) is the fastest to converge, with (10,10) reaching a solution as well. ...
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... in the previous section, we plot performance for (10,10), (30,30), (60,60) values of angle and size thresholds. Figures 16, 17, and 18 show the results of the localizer over three parameter settings. ...
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Deep-sea robotic operations require a high level of safety, efficiency and reliability. In the development stage of such systems, measures have to be taken into account to validate performance in order to assess the achievement of these requirements. In the context of continuous system integration, we proposed a simulation-in-the-loop framework foc...
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... By analogy to a common terminology adopted in sonar imagery [12], we name space ...
This paper presents a method for determining the area explored by a line-sweep sensor during an area-covering mission in a two-dimensional plane. Accurate knowledge of the explored area is crucial for various applications in robotics, such as mapping, surveillance, and coverage optimization. The proposed method leverages the concept of coverage measure of the environment and its relation to the topological degree in the plane, to estimate the extent of the explored region. In addition, we extend the approach to uncertain coverage measure values using interval analysis. This last contribution allows for a guaranteed characterization of the explored area, essential considering the often critical character of area-covering missions. Finally, this paper also proposes a novel algorithm for computing the topological degree in the 2-dimensional plane, for all the points inside an area of interest, which differs from existing solutions that compute the topological degree for single points. The applicability of the method is evaluated through a real-world experiment.
... Exactly as the name suggests, this approach requires local variation in the bathymetry on a scale large enough to be resolved by the acoustic measurement but still small enough to provide unique features on a reasonable scale. Feature-based approaches for underwater localization has previously been investigated for side-scan sonars (Aulinas et al., 2010;King et al., 2017;Nguyen et al., 2012), where the intensity of the acoustic returns are used. Similarly, acoustic backscatter data from a multibeam echosounder has been used to coregister multibeam echosounder transects with side-scan sonar transects (Shang et al., 2019). ...
Autonomous underwater vehicles have become essential tools for the collection of high-resolution bathymetric and geophysical data. An inertial navigation system, aided by a Doppler velocity log and a surface-mounted ultrashort baseline acoustic positioning system, is generally capable of providing sufficient accuracy at conventional survey scales. However, the accuracy decreases with depth, and the resulting relative positioning across different transects may not be sufficient to resolve features of interest at fine scales. This work presents a method for accurate coregistration of the position of adjacent transects. The approach is based on detecting and matching local features in overlapping multibeam echosounder swaths. The navigational errors for the transects are taken to be described by latent Gaussian processes, observed through these matches. The hyperparameters of the Gaussian process are learned from the data themselves and do neither require tuning of filter parameters nor intimate knowledge of the autonomous system or its sensor configuration. The proposed method is robust to outliers by considering a non-Gaussian observation model. The approach is demonstrated on a data set collected at the Arctic Mid-Ocean Ridge (AMOR). The method can be used to construct high-resolution bathymetric models from repeated passes over the same area or to accurately coregister other sensors such as cameras, subbottom profilers, and magnetometers. The primary contribution of this work is the application of feature-based matching of bathymetry with a robust Gaussian process.
... Vision and sonar are the primary sensory systems available to autonomous underwater vehicles (AUVs) for navigation [1][2][3][4]. However, because there are many obstacles near shore and the turbid environment is problematic, vision and sonar systems have many limitations for AUV operation. ...
Fish can sense their surrounding environment by their lateral line system (LLS). In order to understand the extent to which information can be derived via LLS and to improve the adaptive ability of autonomous underwater vehicles (AUVs), a novel strategy is presented, which directly uses the information of the flow field to distinguish the object obstacle. The flow fields around different targets are obtained by the numerical method, and the pressure signal on the virtual lateral line is studied based on the chaos theory and fast Fourier transform (FFT). The compounded parametric features, including the chaotic features (CF) and the power spectrum density (PSD), which is named CF-PSD, are used to recognize the kinds of obstacles. During the research of CF, the largest Lyapunov exponent (LLE), saturated correlation dimension (SCD), and Kolmogorov entropy (KE) are taken into account, and PSD features include the number, amplitude, and position of wave crests. A two-step support vector machine (SVM) is built and used to classify the shapes and incidence angles based on the CF-PSD. It is demonstrated that the flow fields around triangular and square targets are chaotic systems, and the new findings indicate that the object obstacle can be recognized directly based on the information of the flow field, and the consideration of a parametric feature extraction method (CF-PSD) results in considerably higher classification success.
The detection of underwater objects in sonar imagery is a key enabling technique, for applications ranging from mine hunting and seabed characterization to marine archaeology. Owing to the nonhomogeneity of the sonar imagery, the majority of detection approaches are geared toward the detection of features in the spatial domain to identify anomalies in the seabed’s background. Yet, when the seabed is complex and includes rocks and sand ripples, spatial features are hard to discriminate, leading to high false alarm rates. With the aim of validating the detection of man-made objects in complex environments, we utilize the expected spectral diversity of reflections. This way, we can differentiate man-made objects’ reflections from the relatively flat frequency response of natural objects’ reflections, such as rocks. Our solution merges a set of preregistered sonar images of the same scene that are obtained at a different center frequency. For low- or high-resolution sonar images, we apply the Jain’s fairness index or the Kullback–Leibler divergence, respectively, to evaluate the spectral diversity of the reflections of a given region of interest and, thus, detect anomalies across the spectrum domain. We test our algorithm over simulated data and over images collected in three designated sea experiments: a data set that we share with the community. The results show that, compared with benchmark schemes, our solution achieves lower false alarm rates while preserving high detection level.
