AUV Self-localization in Structured Environments Using a Scanning Sonar and an Extended Kalman Filter

Fula, Joao P.; Ferreira, Bruno M.; Oliveira, Antonio J.

doi:10.1109/oceans.2018.8604798

Cited by 3 publications

(1 citation statement)

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“…These datasets consist of a series of intensity measurements, which are organized in the form of intensity arrays of the received acoustic signals. For each scanning angle α i , an ordered set of m intensities values, represented by 8-bit integers, is generated [21]. The total number of measurements bins, m, returned by the sonar for each scanning angle is a function of the maximum range and the bin length for which the sonar is configured.…”

Section: Datasetsmentioning

confidence: 99%

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

Oliveira

Ferreira

Cruz

2021

JMSE

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In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

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Section: Datasetsmentioning

confidence: 99%

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

Oliveira

Ferreira

Cruz

2021

JMSE

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Image segmentation and mapping in an underwater environment using an imaging sonar

Gonçalvès

Ferreira

Alves

et al. 2022

OCEANS 2022, Hampton Roads

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Experimental evaluation of segmentation algorithms for corner detection in sonar images

Oliveira

Ferreira

Cruz

2019

Oceans 2019 MTS/Ieee Seattle

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The development of autonomous underwater vehicles (AUVs) represents one of the latest great achievements of engineering and science for the exploration of the underwater world. The use of these vehicles facilitates the collection of data and monitoring of these environments, allowing us to perform previously impossible operations. Due to the high attenuation suffered by the GPS and other radio-frequency signals underwater, the use of acoustic communications and sensors is predominant. Imaging sonars are capable of measuring the returning echo intensity values from a scanned area. These measurements can be decomposed into an acoustic image of the environment, generally referred to as a sonar image. Mechanically scanned imaging sonars (MSISs) are particularly interesting for being incorporated into an AUV to acquire information in underwater environments. Because of their directionality and beam width, this type of sensor is well suited for obstacle detection and characterization. Corners usually appear very distinct from the rest of the scene in an MSIS image, generally characterized by sharp intensities. The detection of corners is particularly useful in humanstructured environments such as tanks because the knowledge on their location provides a way to compute the vehicle position. The combination of some basic operations typically used for image segmentation has great potential to detect and localize corners in sonar images automatically. This dissertation proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. The developed algorithms are evaluated with ground truth, and their performance is analyzed following a few relevant metrics for autonomous navigation. iii iv The accomplishment of this dissertation marks a significant period in my life because it culminates five years of studies, presenting itself as a test to my autonomy and preparing me to face the challenges that may arise in my professional life. I recognize that none of this could have been achieved without the help of the people mentioned in the following paragraphs. First of all, I would like to express my gratitude to my supervisors, INESC senior researchers Bruno Ferreira and Nuno Cruz for granting me this opportunity, for all the support and guidance provided throughout the development of this dissertation and for the patience and dedication demonstrated. I also want to thank INESC CRAS for providing the space and equipment necessary. Not forgetting their technician Jorge Barbosa for the helping hand provided and for the fellowship spirit. I am eternally grateful to my parents for always believing and supporting me unconditionally. This dissertation is dedicated to them. Lastly but not less important, I want to thank all my friends for the company and support provided during my years at FEUP and above all, for making my life worthwhile.

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AUV Self-localization in Structured Environments Using a Scanning Sonar and an Extended Kalman Filter

Cited by 3 publications

References 1 publication

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

Image segmentation and mapping in an underwater environment using an imaging sonar

Experimental evaluation of segmentation algorithms for corner detection in sonar images

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