Application of acoustic image processing in underwater terrain aided navigation

Song, Ziqi; Bian, Hongyu; Zieliński, A.

doi:10.1016/j.oceaneng.2016.05.039

Cited by 26 publications

(11 citation statements)

References 15 publications

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“…Many scientists globally are working on comparative (terrain reference) navigation [8][9][10]. Most studies have analyzed the shape of the bottom of bodies of water obtained from the depth of the basin.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

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Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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“…In order to verify the superiority of the proposed terrain matching algorithm based on 3DZMs, a terrain matching algorithm (Song et al, 2016) is compared under the same conditions. The feature vector of the algorithm is composed of ten terrain features.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The feature vector of the algorithm is composed of ten terrain features. That is: where E, I, C and L are energy, contrast, correlation and inverse difference moment extracted from the grey level co-occurrence matrix of an image respectively; , σ ′, r, R, H f and H e are mean of elevation, standard deviation of elevation, terrain roughness, correlation coefficient, entropy, and difference entropy of a region, respectively, and α i ( i = 1, 2, · · · , 10) is the weight which has the same value as that in the literature (Song et al, 2016). The definitions of the features can be found in Baraldi and Parmiggiani (1995) and Ma and Zhao (2012).…”

Section: Numerical Experimentsmentioning

confidence: 99%

Real-time Terrain Matching Based on 3D Zernike Moments

2018

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Since the descriptors based on Three-Dimensional (3D) Zernike moments are robust to geometric transformations and noise, they have been proposed for terrain matching. However, terrain matching algorithms based on 3D Zernike Moments (3DZMs) are often difficult to implement in practice since they are computationally intensive. This paper presents a more efficient real-time terrain matching algorithm based on 3DZMs for land applications. Two efficient methods based on coordinate transformation and symmetry are proposed to compute the geometric moments. The impact of the sample difference on the matching result due to heading angle is investigated to prove the feasibility of using a circular template. Consequently, the terrain feature vectors of the reference map can be computed off-line with the circular template to significantly reduce on-line computation. Numerical experiments on a real digital elevation model demonstrate that the proposed algorithm is robust to the heading angle and can be implemented for real-time terrain matching operations.

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“…The terrain factors are correlated with the matching results further by LR (logistics regression), DA (discriminant analysis), neural networks, or other classifications so that the quantitative classifiers can be derived to estimate the matching performance using the terrain factors . Some terrain factors have even been proposed to construct the feature vector for a matching algorithm . However, the basis for describing the terrain elevations using the factors is no more than the statistical analysis, which is not good for understanding the relationship between the terrain factors and the matching performance in the conceptual meaning.…”

Section: Introductionmentioning

confidence: 99%

“…22,23 Some terrain factors have even been proposed to construct the feature vector for a matching algorithm. 24,25 However, the basis for describing the terrain elevations using the factors is no more than the statistical analysis, which is not good for understanding the relationship between the terrain factors and the matching performance in the conceptual meaning. This paper proposes a new method to correlate the terrain factors with the matching performance statistically by factor analysis.…”

Section: Introductionmentioning

confidence: 99%

Impact of terrain factors on the matching performance of terrain‐aided navigation

2019

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Aiming at describing terrain variation in an easily understandable way, a new method is proposed to describe the terrain elevations quantitatively by factor analysis for terrain‐aided navigation. After as many terrain factors as possible are collected describing the terrain elevations from various aspects, they are simplified by correlation to remove the redundant factors. The simplified terrain factors are processed to derive the extracted factors by principal component analysis. With such factor analysis, three extracted factors dubbed “variation,” “similarity,” and “information” are derived in the conceptual level. A classifier is derived by logistic regression using the extracted factors. The percentages of the correct classification are very high both for the suitable and the unsuitable regions of matching in two maps, which proves the effectiveness of the extracted factors for the terrain elevations.

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Application of acoustic image processing in underwater terrain aided navigation

Cited by 26 publications

References 15 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Real-time Terrain Matching Based on 3D Zernike Moments

Impact of terrain factors on the matching performance of terrain‐aided navigation

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