Content
This course follows the book “State Estimation for Robotics” by Timothy Barfoot, Cambridge University Press (available online, follow the link below for a table of contents). Like the book, the course has a considerable mathematical payload. The topics covered include the following: optimization, least squares, linear-Gaussian estimation, nonlinear non-Gaussian estimation, uncertainty propagation, Bayesian filtering, Kalman filtering (KF), extended KF, particle filters, recursive estimation, state parameterizations, Simultaneous Localization and Mapping (SLAM) and continuous-time estimation. The course will provide details on how to tailor general estimation results to robots operating in three-dimensional space, advocating the matrix Lie group approach to handling rotations and poses. For the interested reader, a one-page summary of the history of estimation is provided in Section 1.1 of Barfoot’s book.