Publications from SMaRC

Articles

•  “Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV”, Bhat, C. Panteli, I. Stenius and D. V. Dimarogonas, Journal of Field Robotics, 2023, pp 1-20.
• “A fully-automatic side-scan sonar simultaneous localization and mapping framework,”, J. Zhang, Y. Xie, L. Ling and J. Folkesson, IET Radar Sonar Navig. 1–10, 2023, doi: 10.1049/rsn2.12500
• “Bathymetric Reconstruction From Sidescan Sonar With Deep Neural Networks,”, Y. Xie, N. Bore and J. Folkesson, in IEEE Journal of Oceanic Engineering, vol. 48, no. 2, pp. 372-383, April 2023, doi: 10.1109/JOE.2022.3220330.
• “Neural Shape-From-Shading for Survey-Scale Self-Consistent Bathymetry From Sidescan,”, N. Bore and J. Folkesson, in IEEE Journal of Oceanic Engineering, vol. 48, no. 2, pp. 416-430, April 2023, doi: 10.1109/JOE.2022.3215822.
• “Online Stochastic Variational Gaussian Process Mapping for Large-Scale Bathymetric SLAM in Real Time,”, I. Torroba, M. Cella, A. Terán, N. Rolleberg and J. Folkesson in IEEE Robotics and Automation Letters, vol. 8, no. 6, pp. 3150-3157, June 2023,  doi: 10.1109/LRA.2023.3264750.
• “Implications of Time Variability on a Ricean Noncoherent Model in Acoustic Underwater Communication,”, V. Lidström, 2022 Sixth Underwater Communications and Networking Conference (UComms), Lerici, Italy, 2022, pp. 1-5, doi: 10.1109/UComms56954.2022.9905697.
• “A Framework For Testing Data Driven Underwater Link Adaptation,”, V. Lidström, F. Lindqvist, M. L. Nordenvaad and E. S. Erstorp,  2022 Sixth Underwater Communications and Networking Conference (UComms), Lerici, Italy, 2022, pp. 1-5, doi: 10.1109/UComms56954.2022.9905684.
• “Fully-Probabilistic Terrain Modelling and Localization With Stochastic Variational Gaussian Process Maps,” I. Torroba, C. I. Sprague and J. Folkesson, in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 8729-8736, Oct. 2022, doi: 10.1109/LRA.2022.3182807.
• “Including Heat Balance When Designing the Energy System of Fuel Cell-Powered AUVs”, Chiche, A., Lindbergh, G., Stenius, I., & Lagergren, C. Article in journal, Energies, 2021
• “Inferring depth contours from sidescan sonar using convolutional neural nets.”, Xie, Y., Bore, N. and Folkesson, J. (2020), IET Radar Sonar Navig., 14: 328-334, 2020, doi: 10.1049/iet-rsn.2019.0428.
• “Modeling and Simulation of Sidescan Using Conditional Generative Adversarial Network,” N. Bore and J. Folkesson, in IEEE Journal of Oceanic Engineering, vol. 46, no. 1, pp. 195-205, Jan. 2021, doi:10.1109/JOE.2020.2980456.
• “Pathways and modification of warm water flowing beneath Thwaites ice shelf, West Antarctica” A. K. Wåhlin, A. Graham, K. A. Hogan, B. Y. Queste, L. Boehme, R. Larter, E. Pettit, J. Wellner and K. J. Heywood, Article in journal, 2021
• “Design of experiment to predict the time between hydrogen purges for an air-breathing PEM fuel cell in dead-end mode in a closed environment” Chiche, A., Lindbergh, G., Stenius, I., & Lagergren, C. Article in journal, International Journal of Hydrogen Energy, 2021
• “A strategy for sizing and optimizing the energy system on long-range AUVs”, Chiche, A., Lindbergh, G., Stenius, I., & Lagergren, C. Article in journal, IEEE Journal of Oceanic Engineering, 2021
• “Feasibility and impact of a Swedish fuel cell-powered rescue boat”, Chiche, A., Andruetto, C., Lagergren, C., Lindbergh, G., Stenius, I., & Peretti, L. Article in journal, Ocean Engineering, 2021
• “Evaluation of Polar-Coded Noncoherent Acoustic Underwater Communication,” V. Lidström, in IEEE Journal of Oceanic Engineering, doi: 10.1109/JOE.2022.3227233.
• “Polar Coded Non-Coherent Acoustic Underwater Communication,” V. Lidström,  2021 Fifth Underwater Communications and Networking Conference (UComms), Lerici, Italy, 2021, pp. 1-5, doi: 10.1109/UComms50339.2021.9598134.
• “Super Permutation Frequency Shift Keyed Underwater Acoustic Communication,” V. Lidström,  IEEE Journal of Oceanic Engineering, in press.
• “Glider performance analysis and intermediate-fidelity modelling of underwater vehicles“, C. Deutsch, J. Kuttenkeuler, T. Melin, Article in journal, Ocean Engineering, Elsevier, 2020
• “PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM”, Torroba, I., Sprague, C. I., Bore, N., & Folkesson, J., Article in journal, IEEE Robotics and Automation Letters, 5(3), 4078-4085, 2020.
• “Ice front blocking of ocean heat transport to an Antarctic ice shelf“, A. K. Wåhlin, N. Steiger, E. Darelius, K. M. Assmann, M. S. Glessmer, H. K. Ha, L. Herraiz-Borreguero, C. Heuzé, A. Jenkins, T. W. Kim, A. K. Mazur, J. Sommeria & S. Viboud, Article in journal, February 26, 2020
• “High-resolution bathymetric mapping reveals subaqueous glacial landforms in the Arctic alpine lake Tarfala, Sweden“, Nina Kirchner, Riko Noormets, Jakob Kuttenkeuler, Elias Strandell Erstorp, Erik Schytt Holmlund, Gunhild Rosqvist, Per Holmlund, Marika Wennbom, Torbjörn Karlin, Article in journal, September 3, 2019

Conference papers

•  “Controlling an Underactuated AUV as an Inverted Pendulum using Nonlinear Model Predictive Control and Behavior Trees”, Bhat, and I. Stenius, IEEE International Conference on Robotics and Automation (ICRA), London, 2023, pp 1-6.
• “Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM,”J. Tan, I. Torroba, Y. Xie and J. Folkesson, 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 3131-3137, doi: 10.1109/ICRA48891.2023.10160783.
• “Underwater Image Classification via Multiview-based Auxiliary Learning,” Athanasiadis, N. Bore and J. Folkesson, OCEANS 2022, Hampton Roads, Hampton Roads, VA, USA, 2022, pp. 1-7, doi:10.1109/OCEANS47191.2022.9977242.
• “Underwater Caging and Capture for Autonomous Underwater Vehicles” , Özer Özkahraman; Petter Ögren, Conference paper 2020
• “Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions”, Özer Özkahraman; Petter Ögren, Conference paper, 2020
• “Lambert’s Cosine Law and Sidescan Sonar Modeling,” J. Folkesson, H. Chang and N. Bore, 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St Johns, NL, Canada, 2020, pp. 1-6
• “Learning How to Learn Bathymetry,” C. I. Sprague and P. Ögren, 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St Johns, NL, Canada, 2020, pp. 1-2, Conference paper 2020
• “Autonomous Underwater Vehicles Symposium (AUV)” S. Bhat et al., “A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration,” 2020 IEEE/OES, St Johns, NL, Canada, 2020, pp. 1-8, doi, Conference paper 2020
• “Latent Space Metric Learning For Sidescan Sonar Place Recognition,” M. Larsson, N. Bore and J. Folkesson, 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St Johns, NL, Canada, 2020, pp. 1-6, doi: Link: https://doi.org/10.1109/AUV50043.2020.9267885
• “Energy Management Strategies for Fuel Cell-Battery Hybrid AUVs,” C. Deutsch, A. Chiche, S. Bhat, C. Lagergren, G. Lindbergh and J. Kuttenkeuler, 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St Johns, NL, Canada, 2020, pp. 1-6, doi: Conference paper 2020
  Link: https://doi.org/10.1109/AUV50043.2020.926793
• ”Energy Management Strategies for Fuel Cell-Battery Hybrid AUVs”, Deutsch, C., Chiche, A., Bhat, S., Lagergren, C., Lindbergh, G., & Kuttenkeuler, J. Conference paper, 2020
• ”Sizing the energy system on long-range AUV” Chiche, A., Lindbergh, G., Stenius, I., & Lagergren, C. Conference paper, 2018
• “Automated Underwater Pipeline Damage Detection using Neural Nets”, Jiajun Shi, Wenjie Yin, Yipai Du, John Folkesson, Conference paper, August 21, 2019
• “Non-Coherent Acoustic Modulation for Energy Constrained Underwater Platforms“, Viktor Lidström, Elias S. Erstorp, Magnus L. Nordenvaad, Peter Sigray, Jakob Kuttenkeuler, Conference proceedings, 2019
• “Object Recognition in Forward Looking Sonar Images using Transfer Learning“, Louise Rixon Fuchs, Andreas Gällström, John Folkesson, Conference paper, May 7, 2019
• “Towards Blended Reactive Planning and Acting using Behavior Trees“, Michele Colledanchise, Diogo Almeida, Petter Ögren, Conference paper, April 9, 2019
• “Towards a Cyber-Physical System for Hydrobatic AUVs”, Bhat S., Stenius I., Bore N., Severholt J., Ljung C., Balmori I.T., Conference proceedings, 2019
• “Towards Autonomous Industrial-Scale Bathymetric Surveying”, Ignacio Torroba, Nils Bore, John Folkesson, Conference paper, 2019
• “Deep Learning Based Technique for Enhanced Sonar Imaging”, Rixon Fuchs L, Gällström A, Conference paper, 2019
• “Enhanced Sonar Image Resolution using Compressive Sensing Modelling”, Gällström A, Rixon Fuchs L, Larsson C, Conference paper, 2019
• “Loop Closure Detection Through Environmental Indicators In Underwater SLAM,” I. Torroba, N. Bore, A. Wåhlin and J. Folkesson, OCEANS 2019 – Marseille, Marseille, France, 2019, pp. 1-7, doi: 10.1109/OCEANSE.2019.8867097.
• “A Comparison of Submap Registration Methods for Multibeam Bathymetric Mapping,” I. Torroba, N. Bore and J. Folkesson, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 2018, pp. 1-6, doi: 10.1109/AUV.2018.8729731.
• “Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry,” N. Bore, I. Torroba and J. Folkesson, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 2018, pp. 1-6, doi: 10.1109/AUV.2018.8729748.
• “Learning a family of optimal state feedback controllers“, CI Sprague, D Izzo, P Ögren, Conference paper, 2019
• “Adding neural network controllers to behavior trees without destroying performance guarantees“, CI Sprague, P Ögren, Conference paper, 2018
• “Design of an AUV Research Platform for Demonstration of Novel Technologies“, ​​C. Deutsch, L. Moratelli, S. Thuné, J. Kuttenkeuler, F. Söderling, Conference paper, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)
• “Improving the Modularity of AUV Control Systems using Behaviour Trees“, Christopher Sprague et al, Conference paper, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018, Porto, Portugal, 6-9 November 2018
• ”Hydrobatics: A review of trends, challenges and opportunities for efficient and agile underactuated AUVs”, S. Bhat, I. Stenius, Conference paper, IEEE AUV 2018, Porto, Portugal, November, 2018

Master theses

• ‘Multi-Resolution Inference of Bathymetry From Sidescan Sonar’, Z. Ji, Dissertation, 2023.
• ‘Reinforcement Learning for Hydrobatic AUVs’, G. Woźniak, Dissertation, 2022.
• ‘AUV SLAM constraint formation using side scan sonar’, M. Schouten, Dissertation, 2022.
• ‘Pixel correspondences for SLAM using sidescan sonar with canonical representations’, W. Xu, Dissertation, 2022.
• ‘Submap Correspondences for Bathymetric SLAM Using Deep Neural Networks’, J. Tan, Dissertation, KTH Royal Institute of Technology, Stockholm, 2022.
• ‘Underwater Rao-Blackwellized Particle Filter SLAM using Stochastic Variational Gaussian Processes maps’, S. Olsson, Dissertation, 2021.
• ‘Local Feature Correspondence on Side-Scan Sonar Seafloor Images’, L. Ling, Dissertation, 2021.
• ‘Canonical Representation of sidescan sonar images for robotics application’, Dissertation, H. Chang, 2021.
• ‘Combining Sidescan Sonar and Multibeam Echo Sounder to Improve Bathymetric Resolution per Ping’, F. Hestell, Dissertation, 2021.
• ‘Acoustic-Inertial Forward-Scan Sonar Simultaneous Localization and Mapping’, A. Teran Espinoza, Dissertation, 2020.
• ‘Monocular Visual Odometry for Autonomous Underwater Navigation : An analysis of learning-based monocular visual odometry approaches in underwater scenarios’, A. Caraffa, Dissertation, 2021.
• “Informative path planning for algae farm surveying“, Corentin Guy Claude Chauvin-Hameau, Master thesis, 2020
• “Generation of a full-envelope hydrodynamic database for hydrobatic AUVs: Combining numerical, semi-empirical methods to calculate AUV hydrodynamic coefficients“, Tianlei Miao, Master thesis, 2019
• “Machine Learning for Inferring Depth from Side-scan Sonar Images”, Yiping Xie, Master thesis, 2019
• “Machine Learning for Inferring Sidescan Images from Bathymetry and AUV Pose”, Zitao Zhang, Master thesis, July 10, 2019
• “Underwater Change Detection by Fusing Multiple Sonar Images”, Daniel Eriksson, Master thesis, 2019