Research
Lets work together
We continue to participate in research and innovation activity on topics such as robotics, oceanography, biology, statistics, and artificial intelligence. We are open to partnerships with national and international organizations to develop new and interdisciplinary solutions. Our goal is to actively contribute towards improving our understanding of our worlds environments through sustainable technological solutions.
A curated list over research that has been conducted by the Skarv-team is presented below. The research spans from marine robotics, underwater navigation and control, informative sampling, machine intelligence, and statistics.
- Fossum, Trygve Olav, et al. “Underwater autonomous mapping and characterization of marine debris in urban water bodies.” arXiv preprint arXiv:2208.00802 (2022).
- Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D., & Rajan, K. – Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling – Annals of Applied Statistics (2021).
- Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D., & Rajan, K. – Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling – Annals of Applied Statistics (2021).
- T. O. Fossum, P. Norgren, I. Fer, F. Nilsen, Z. C. Koenig and M. Ludvigsen, “Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle,” (2021) in IEEE Journal of Oceanic Engineering, doi: 10.1109/JOE.2021.3070912.
- Lecture by Trygve Olav Fossum at the University of Washington and the Paul G. Allen school of computer science and engineering – Searching for information in the ocean – Adaptive Sampling of Dynamic Processes in Coastal Areas and Fjords using Autonomous Underwater Vehicles.
- (2020) Observations of Turbulence at a Near-Surface Temperature Front in the Arctic Ocean. Journal of Geophysical Research (JGR): Oceans.
- Bremnes, J.E., Norgren, P., Sørensen, A.J., Thieme, C.A., and Utne, I.B. Intelligent Risk-Based Under-Ice Altitude Control for Autonomous Underwater Vehicles. MTS/IEEE OCEANS, 2019.
- Bremnes, J. E., Thieme, C. A., Sørensen, A. J., Utne, I. B., & Norgren, P. (2020). A Bayesian Approach to Supervisory Risk Control of AUVs Applied to Under-Ice Operations. Marine Technology Society Journal, 54(4), 16-39.
- Fossum, T.O., 2019. Adaptive Sampling for Marine Robotics.
- Fossum, T.O., Fragoso, G.M., Davies, E.J., Ullgren, J., Mendes, R., Johnsen, G., Ellingsen, I.H., Eidsvik, J., Ludvigsen, M. and Rajan, K., 2019, February. Toward adaptive robotic sampling of phytoplankton in the coastal ocean. American Association for the Advancement of Science.
- Fossum, T.O., Eidsvik, J., Ellingsen, I., Alver, M.O., Fragoso, G.M., Johnsen, G., Mendes, R., Ludvigsen, M. and Rajan, K., 2018. Information‐driven robotic sampling in the coastal ocean. Journal of Field Robotics, 35(7), pp.1101-1121.
- Fossum, T.O., Ludvigsen, M., Nornes, S.M., Rist-Christensen, I. and Brusletto, L., 2016, September. Autonomous robotic intervention using ROV: an experimental approach. In OCEANS 2016 MTS/IEEE Monterey (pp. 1-6). IEEE.
- Fossum, T.O., 2016. Intelligent autonomous underwater vehicles. Norwegian University of Science and Technology, Tech. Rep.
- Fossum, T.O. et al. (2019) ‘Compact models for adaptive sampling in marine robotics’, The International Journal of Robotics Research.
- Ludvigsen, M., Albrektsen, S.M., Cisek, K., Johansen, T.A., Norgren, P., Skjetne, R., Zolich, A.P., Dias, P.S., Ferreira, S., Sousa, J. B., Fossum, T.O., Sture, Ø., Krogstad, T.R., Midtgaard, Ø., Hovstein, V.E. and Vågsholm, E. Network of heterogeneous autonomous vehicles for marine research and management. MTS/IEEE OCEANS, 2016.
- Misimi, E., Øye, E.R., Sture, Ø., Mathiassen, J.R., 2017. Robust classification approach for segmentation of blood defects in cod fillets based on deep convolutional neural networks and support vector machines and calculation of gripper vectors for robotic processing. Computers and Electronics in Agriculture 139, 138–152.
- Ilker Fer, Frank Nilsen, Anthony Bosse, Eva Falck, Trygve Fossum, Lars R Hole, Zoé Koenig, Eivind Kolås, Aleksander D Libæk, Ben Lincoln, Martin Ludvigsen, Marika Marnela, Malte Müller, Petter Norgren, Inger Lise Næss, Jean Rabault, Andrew Siedl, Ragnheid Skogseth, Inga B Utkilen, Physical Process Cruise 2018 – Nansen Legacy, The Nansen Legacy Report Series, 2018.
- Norgren, P. and Skjetne, R.. A multibeam-based slam algorithm for iceberg mapping using auvs. IEEE Access, 6, 2018,26318-26337.
- Norgren, P., Ludvigsen, M., Ingebretsen, T. and Hovstein, V.E. Tracking and remote monitoring of an autonomous underwater vehicle using an unmanned surface vehicle in the Trondheim fjord. OCEANS 2015-MTS/IEEE Washington. IEEE, 2015. p. 1-6.
- Norgren, P. and Skjetne, R. Line-of-sight iceberg edge-following using an AUV equipped with multibeam sonar. IFAC-PapersOnLine, 48(16), (2015), 81-88.
- Sture, Ø., Norgren, P., Ludvigsen, M., 2020. Trajectory Planning for Navigation Aiding of Autonomous Underwater Vehicles. IEEE Access.
- Sture, Ø., Snook, B., Ludvigsen, M., 2019. Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration. Minerals 9, 694.
- Sture, Ø., Fossum, T.O., Ludvigsen, M., Wiig, M.S., 2018a. Autonomous Optical Survey Based on Unsupervised Segmentation of Acoustic Backscatter. p. 8.
- Sture, Ø., Ludvigsen, M., Scheide, M.S., Thorsnes, T., 2018b. Recognition of Cold-Water Corals in Synthetic Aperture Sonar Imagery. p. 6.
- Sture, Ø., Ludvigsen, M., Søreide, F., Aas, L.M.S., 2017a. Autonomous underwater vehicles as a platform for underwater hyperspectral imaging, in: Proc. OCEANS 2017 – Aberdeen, IEEE, Aberdeen, United Kingdom, pp. 1–8.
- Sture, Ø., Syre Wiig, M., Fossum, T.O., 2017b. NTNU-FFI Cruise 2017 – Hugin autonomy integration (DUNE, T-REX) (No. 2017–1), NTNU Cruise reports. NTNU Open.
- Sture, Ø., Øye, E.R., Skavhaug, A., Mathiassen, J.R., 2016. A 3D machine vision system for quality grading of Atlantic salmon. Computers and Electronics in Agriculture 123, 142–148.
