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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.
Date : 2008-10-13 Size : 118.58kb User : 阳关

文件实现求解线性方程组,多项式插值或积分,包含gauss消去,迭代等-File to realize solving system of linear equations, polynomial interpolation or integral, containing gauss elimination, iteration, etc
Date : 2026-01-01 Size : 83kb User : olruot
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