Kalman / EKF / UKF
Optimal recursive estimation for (nearly) linear-Gaussian systems.
hardState Estimation
Why it matters in robotics
The Kalman filter is THE workhorse of robotic state estimation โ localization, IMU fusion, tracking. Interviewers love it because it tests probability, linear algebra, and systems thinking at once. You'll be asked why it's optimal, when it breaks, and how the EKF/UKF cope with nonlinearity.
Application focus
The same topic, tailored to the robot you're building. Your choice is remembered across the roadmap and every topic.
Select an application above.
At a glance
The Kalman filter is a recursive predict โ update loop over a Gaussian belief.
What to study
- โLinear KF: predict/update equations & the Kalman gain
- โWhy it's the optimal linear-Gaussian estimator (MMSE)
- โEKF: linearization via Jacobians, and its failure modes
- โUKF: the unscented transform vs. EKF
- โTuning Q and R; observability & filter divergence
Study by time budget
Pick the path that fits the time you have before your interview.