Extensions with RFSs in SLAM - Mullane, J.; Vo, Ba-Ngu; Adams, M.; Vo, B. (2011)
This book demonstrates that the inherent uncertainty of feature maps and feature map measurements can be naturally encapsulated by random finite set models, and subsequently in Chapter 5 proposed the multi-feature RFSSLAM framework and recursion of equations 5.5 and 5.6. The SLAM solutions presented thus far focussed on the joint propagation of the the first-order statistical moment or expectation of the RFS map, i.e. its Probability Hypothesis Density, v k , and the vehicle trajectory. Recall from Chapter 3 that the integral of the PHD, which operates on a feature state space, gives the expected number of features in the map, at its maxima represent regions in Euclidean map space where features are most likely to exist.