Extended Kalman Filtering and Gaussian Belief Space Planning through Sequential Convex Programming [40pts]
نویسنده
چکیده
In this question you get to implement an extended Kalman Filter (EKF) for state estimation for nonlinear dynamics and observation models. Notes: Let x ∈ RxDim be the system state, u ∈ RuDim denote the control input applied to the system, and z ∈ RzDim be the vector of observations obtained about the system state using sensors. We are given a discrete-time stochastic dynamics model that describes how the system state evolves and an observation model that relates the obtained observations to the state, given here in state-transition notation: xt+1 = f(xt,ut,qt), qt ∼ N (0, Q), (1) zt = h(xt, rt), rt ∼ N (0, R). (2)
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