Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle

Filters (PFs) that exploit conditional dependencies between

parts of the state to estimate. By doing so, RBPFs can

improve the estimation quality while also reducing the overall

computational load in comparison to original PFs. However,

the computational complexity is still too high for many

real-time applications. In this paper, we propose a modified

RBPF that requires a single Kalman Filter (KF) iteration per

input sample. Comparative experiments show that while good

convergence can still be obtained, computational efficiency is

always drastically increased, making this algorithm an option

to consider for real-time implementations.