%PDF-1.4 % 1 0 obj << /Type /Pages /Count 2 /Kids [ 2 0 R 117 0 R ] >> endobj 2 0 obj << /Type /Page /Parent 1 0 R /Resources << /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] /Font << /QuickPDFF2df08deb 71 0 R /QuickPDFF67275ad9 116 0 R >> >> /Contents [ 5 0 R ] /MediaBox [ 0 0 595.2756 841.8898 ] /CropBox [ 0 0 595.2756 841.8898 ] >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R /Metadata 119 0 R >> endobj 4 0 obj << /Producer (Debenu Quick PDF Library 11.15 \(www.debenu.com\)) /Creator (Debenu Quick PDF Library 11.15 \(www.debenu.com\)) /CreationDate (D:20230622205849+02'00') /ModDate (D:20230622205855+02'00') >> endobj 5 0 obj << /Length 3672 /LC /iSQP >> stream 0 Tr /QuickPDFF67275ad9 14 Tf 0 0 0 rg 100 Tz 0 Tw 0 Tc 0 Ts BT 1 0 0 1 68.2058 770.8329 Tm (Senses for Submarines: Concepts for Optical- and Acoustic-Based Odometry)Tj 1 0 0 1 181.3678 752.1709 Tm (and SLAM for Underwater Navigation)Tj ET /QuickPDFF67275ad9 11 Tf 0 0 0 rg BT 1 0 0 1 125.9003 717.2179 Tm (Lukas Klatt, Niklas-Maximilian Schild and Harald Sternberg \(Germany\))Tj ET /QuickPDFF67275ad9 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 675.188 Tm 14.663 TL (Key words: )' ET /QuickPDFF2df08deb 11 Tf 0 0 0 rg BT 1 0 0 1 141.7323 675.188 Tm 14.454 TL (Hydrography; Positioning; AUV; autonomy; pipeline inspections)' ET /QuickPDFF67275ad9 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 646.8416 Tm 14.663 TL (SUMMARY)' ET /QuickPDFF2df08deb 10 Tf 0 0 0 rg BT 1 0 0 1 56.6929 163.8497 Tm 13.14 TL (__________________________________________________________________________________________)' ()' (Senses for Submarines: Concepts for Optical- and Acoustic-Based Odometry and SLAM for Underwater Navigation)' (\(12148\))' (Lukas Klatt, Niklas-Maximilian Schild and Harald Sternberg \(Germany\))' ()' (FIG Working Week 2023)' (Protecting Our World, Conquering New Frontiers )' (Orlando, Florida, USA, 28 May1 June 2023)' ET /QuickPDFF2df08deb 11 Tf 0 0 0 rg BT 1 0 0 1 56.6929 618.4951 Tm 14.454 TL (Autonomous underwater vehicles \(AUV\) offer a great potential for hydrography and exploration of the deep)' (sea. In the CIAM research project \(Comprehensive integrated and fully autonomous subsea monitoring\),)' (funded by the German Ministry of Economy, various AUVs are developed to monitor critical infrastructure,)' (especially pipelines, in the deep sea. They are equipped with a variety of acoustic, electromagnetic, and)' (optical sensors recording the condition of the pipeline and thus make damage and leaks detectable. The)' (autonomous deployment allows for savings in personnel, an improved data basis, and a reduction in costs.)' (Applying a port-to-port concept, further costs can be saved by following the pipeline and omitting the)' (mothership. Conventionally, the navigation of AUVs has been based on inertial navigation. To ensure the)' (reliability it can be coupled with updates from a surface mothership via an Ultashort Base Length modem)' (\(USBL\) or an array of installed underwater antennas that provide extrinsic pose estimation via Long Base)' (Length \(LBL\). This methodology can be considered expensive and not robust due to the high value inertial)' (sensor technology and the challenging and error-prone acoustic communication technique. To address this)' (issue the objective of this paper is developing an improved navigation concept with extrinsic sensors to)' (ensure the collected data to be geo-referenced and a robust control of the AUV. For monitoring the pipelines,)' (the versatile sensors are used to generate 3D models of the pipeline. In addition to the actual data evaluation)' (and damage recognition in postprocessing, the methods for mapping the environment are also applicable for)' (odometry, SLAM-based navigation and mission planning control. Hence, in the context of this paper,)' (alternative concepts for navigation solutions are elaborated that use the detection of features in the AUVs)' (operational environment to ensure simultaneous localisation and mapping. For this purpose, methods of)' (environment representation are used by evaluating sensor data via artificial intelligence, machine learning)' (and deterministic analysis to detect features. The use of a Madgwick filter offers promising results, if the)' (course change of the pipeline determined by the forward-looking sonar is integrated as control input. )' ET endstream endobj 6 0 obj << /Length 316 /Filter /FlateDecode >> stream xMRKnCAۿSpk`y^Uu[jIɒT%C/:b}Rx\%=
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