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The aim of this project is to develop algorithms that allow robust navigation of AUVs while submerged.  We are interested in situations where current solutions such as Long-Base-Line and Ultra-Short-Base-Line are not a practical options.

The algorithms include  SLAM solutions based on camera and/or sonar sensors. In this sub-project, from the earliest stage, focus is directed towards data collection. Data will be collected from more benign environments such as MMT survey data or data from maritime underwater robots in the SEAFARM.

To prepare for Arctic environment data will also be collected from ROV’s in the Antarctic to facilitate realistic testing of the SLAM solutions.  Later in the project we will run the SLAM on the Maritime Underwater Robots in the water but there are several options for that of varying degrees of difficulty and impact.


John Folkesson, assoc. Professor Robotics, Perception and Learning KTH – Project leader

Ignacio Torroba – PhD student

Nils Bore – Post-Doc


Illustration of underwater navigation.