Participants will learn theoretical foundations and relevant algorithms developed in the field of state estimation for robotics. State estimation is key for determining unknown variables in dynamical systems. In robotics it is paramount to determine the state of a robot (a set of quantities, such as position, orientation, and velocity) because once known it fully describes the robot’s motion over time. It is often closely identified with Bayesian filtering and it finds applications in SLAM (Simultaneous Localization and Mapping).
This course closely follows the book State Estimation for Robotics from
Prof. T. Barfoot:
http://asrl.utias.utoronto.ca/~tdb/bib/barfoot_ser17.pdf.
Upon completing the module, participants will have an overview of the field of state estimation and its toolbox. Students will be able to understand robotics systems on ego-motion / attitude estimation, sensor fusion and SLAM. They will be able to identify key performance metrics, applications, advantages and disadvantages of the methods in order to pick the best tool for the job. - Trainer/in: Guillermo Gallego
- Trainer/in: Bianca Mestre