Object Recognition and Coordinate Referencing of an Autonomous Underwater Vehicle to Objects via Video Stream

• Published in: Programming and computer software 2022-10, Vol.48 (5), p.301-311
• Main Authors: Bobkov, V. A., Kudryashov, A. P., Inzartsev, A. V.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Autonomous underwater vehicles; Camcorders; Cameras; Coherence; Computer Science; Coordinate transformations; Coordination; Criteria; Image processing; Infrastructure; Inspection; Navigation systems; Object recognition; Operating Systems; Sensors; Software Engineering; Software Engineering/Programming and Operating Systems; Three dimensional models; Underwater pipelines; Vector processing (computers); Video data; Virtual reality
• ISSN: 0361-7688; 1608-3261
• DOI: 10.1134/S0361768822050024

This paper is devoted to the important problem of subsea infrastructure inspection using autonomous underwater vehicles (AUVs). The accomplishment of the inspection mission requires high accuracy of AUV coordination with respect to objects, which is not always provided by standard hydroacoustic equipment. That is why we propose an approach based on stereo image processing that can provide submeter accuracy of AUV coordination required under subsea conditions. In the framework of a unified approach based on the use of an a priori formed point model of an object and the application of a structural coherence criterion, we propose and analyze several methods for recognition of underwater objects and coordinate referencing of the AUV to these objects. As a geometric model of an object, we consider its representation based on characteristic points, which determines the spatial structure of the object. In addition to the standard method for object model construction from preliminary measurements, a new method is proposed, which is based on automated technique of model generation from images offline. The proposed object recognition methods use the structural coherence criterion to match 3D point clouds with object models. The 3D point clouds are formed from stereo images captured by a camera using visual navigation. Feature points are generated and matched using the SURF detector and Harris corner detector. To improve the reliability of object identification, we propose joint processing of source images with their vectorized forms. The use of line segments obtained by vectorization significantly increases the number of identified characteristic points when matching a 3D point cloud with a model. Based on the identified points of an object, the matrix of AUV coordinate referencing to this object is computed. The efficiency of the considered methods is estimated and compared. In the computational experiments with model scenes, a virtual geometric model of a distributed subsea production system is used. The experiments on real-world data are carried out under laboratory conditions with the use of a Karmin2 stereo camera (Nerian’s 3D stereo camera, baseline 25 cm).