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Underwater localization and mapping for UUVs operating in fish farms
The fish farming industry has seen a rapid growth over the last decades and is today a key provider of seafood [1]. The properties of seawater put limitations on sensor systems that can be used for underwater navigation. Moreover, additional challenges related to robust localization and mapping are imposed by the fish farm environment that must be accounted for when utilizing Unmanned Underwater Vehicles (UUVs) [2].
This thesis aims to develop methods for robust localization and mapping utilizing dataset from industrial scale fish farm of SINTEF ACE facilities [3]. In addition to access to relevant datasets from acoustic and vision-based systems, there will be support from SINTEF ACE full scale aquaculture laboratory to obtain further information and inputs on the relevance of the work [3]. Such methods will contribute to extending the capabilities of UUVs operating in complex underwater environments by facilitating future contributions related to navigation, planning, guidance, control, decision support and autonomous functionalities.
References:
[1] Martin Føre et al. “Precision fish farming: A new framework to improve production in aquaculture”. Biosystems Engineering, 2017
[2] Kelasidi, E. and Svendsen, E. (2022). Robotics for Sea-Based Fish Farming, 1–20. Springer International Publishing, Cham. doi:10.1007/978-3-030-89123-7 202-1
[3] SINTEF ACE. https://www.sintef.no/en/all-laboratories/ace/
This thesis aims to develop methods for robust localization and mapping utilizing dataset from industrial scale fish farm of SINTEF ACE facilities [3]. In addition to access to relevant datasets from acoustic and vision-based systems, there will be support from SINTEF ACE full scale aquaculture laboratory to obtain further information and inputs on the relevance of the work [3]. Such methods will contribute to extending the capabilities of UUVs operating in complex underwater environments by facilitating future contributions related to navigation, planning, guidance, control, decision support and autonomous functionalities.
References: [1] Martin Føre et al. “Precision fish farming: A new framework to improve production in aquaculture”. Biosystems Engineering, 2017 [2] Kelasidi, E. and Svendsen, E. (2022). Robotics for Sea-Based Fish Farming, 1–20. Springer International Publishing, Cham. doi:10.1007/978-3-030-89123-7 202-1 [3] SINTEF ACE. https://www.sintef.no/en/all-laboratories/ace/
- Perform a literature review on current status and challenges in fish farming, suited sensor systems for localization and mapping techniques used in underwater environments
- Develop methods to obtain the relative distance and orientation of the vehicle from net pen
- Co-optimization of diverse exteroceptive (e.g., visual, DVL, echosounders) and proprioceptive (e.g., inertial sensors) cues by relevant methods for this task
- Assess the performance of the sensors and implemented methods using experimental data
- Perform a literature review on current status and challenges in fish farming, suited sensor systems for localization and mapping techniques used in underwater environments - Develop methods to obtain the relative distance and orientation of the vehicle from net pen - Co-optimization of diverse exteroceptive (e.g., visual, DVL, echosounders) and proprioceptive (e.g., inertial sensors) cues by relevant methods for this task - Assess the performance of the sensors and implemented methods using experimental data
- Highly motivated student
- Experience with computer vision
- Experience with C/C++ and/or Python programming is desired
- Highly motivated student - Experience with computer vision - Experience with C/C++ and/or Python programming is desired