Ignacio Torroba Balmori
Postdoc
Details
Researcher
About me
I'm a postdoctoral researcher at the division of Aerospace, Mobility and Naval Architecture, and the division of Robotics, Perception and Learning (RPL), working under the supervision of Ivan Stenius and John Folkesson.
I did my PhD at RPL under John's supervision in Simultaneous Localization and Mapping (SLAM) for autonomous underwater vehicles (AUVs). I'm currently still working in underwater SLAM with different types of sonars and cameras, but I'm also starting to focus on path planning, control and system identification for vehicles both in open waters and confined spaces.
My research in field robotics is very hands-on and problem-oriented, with my current areas of interest ranging from autonomous seabed surveying, algae farm monitoring and seawed mapping. I'm also actively involved in developing the AUV SAM, in the picture, and expanding the autonomy modules of a Kongsberg Hugin, a BlueROV2, our USV FloatSAM and the AUV Lolo, also under development at KTH.
I'm always looking for motivated students with an interest in field robotics. My theses topics offers are most likely not up to date, but if you think we can be a good fit, contact me and we'll design a topic for you.
Former master thesis students:
- Stine Olsson: Underwater Rao-Blackwellized Particle Filter SLAM using Stochastic Variational Gaussian Processes maps
- Jiarui Tan: Submap Correspondences for Bathymetric SLAM Using Deep Neural Networks
- Julian Valdez: AUV SLAM for Algae Farm Inspection: 3D Mosaicing using Sonar-Based SLAM
- Koray Amico: Exploring Simultaneous Localization and Mapping for Multiple Autonomous Underwater Vehicles: Resampling strategies in a Rao-Blackwellized particle filter implementation
- Alexander Kiessling: Efficient Non-Myopic Layered Bayesian Optimization For Large-Scale Bathymetric Informative Path Planning
Courses
Introduction to Robotics (DD2410), assistant | Course web
Machine Learning (DD2421), assistant | Course web
Underwater Technology (SD2709), teacher | Course web