Nonlinear systems provide additional challenges for robotic state estimation. We provide a derivation of the famous extended Kalman filter (EKF) and then go on to study several generalizations and extensions of recursive estimation that are commonly used: the Bayes filter, the iterated EKF, the particle filter, and the sigmapoint Kalman filter. We return to batch estimation for nonlinear systems, which we connect more deeply to numerical optimization than in the linear-Gaussian chapter. We discuss the strengths and weaknesses of the various techniques presented and then introduce sliding-window filters as a compromise between recursive and batch methods. Finally, we discuss how continuous-time motion models can be employed in batch trajectory estimation for nonlinear systems.