Resampling-free Particle Filters in High-dimensions

arXiv:2404.13698v1 Announce Type: cross
Abstract: State estimation is crucial for the performance and safety of numerous robotic applications. Among the suite of estimation techniques, particle filters have been identified as a powerful solution due to their non-parametric nature. Yet, in high-dimensional state spaces, these filters face challenges such as 'particle deprivation' which hinders accurate representation of the true posterior distribution. This paper introduces a novel resampling-free particle filter designed to mitigate particle deprivation by forgoing the traditional resampling step. This …

abstract applications arxiv challenges cs.lg cs.ro dimensions face filters free nature non-parametric parametric particle performance representation resampling robotic safety solution spaces state stat.ml type