In Probabilistic Robotics, page 322, the EKF SLAM update step is shown below. enter image description here

My question is, why is for every observed feature, $\bar{\mu}_t,\bar{\Sigma}_t$ are overwritten in lines 23,24 by the most likely observation in every new loop? It just seems to me, if there are N observed features, then 0th to N-1 th features aren't even accounted for in the final $\mu_t, \Sigma_t$ in line 26 and 27, only the Nth feature and its ML correspondence is updating $\mu_t, \Sigma_t$. I think I might be wrong about my understanding, but I don't see how the subsequent loops account for the previous loops' ML correspondences.


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