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Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit condi - 资源详细说明
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.
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit condi - 源码文件列表
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