Resampling-free Particle Filters in High-dimensions
This addresses a critical bottleneck for robotic systems with high degrees of freedom, though it is an incremental improvement over existing particle filter methods.
The paper tackles particle deprivation in high-dimensional state estimation by introducing a resampling-free particle filter, which theoretically provides near-accurate posterior representation and is validated empirically on synthetic and 6D pose estimation tasks.
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 ensures a broader and more diverse particle set, especially vital in high-dimensional scenarios. Theoretically, our proposed filter is shown to offer a near-accurate representation of the desired posterior distribution in high-dimensional contexts. Empirically, the effectiveness of our approach is underscored through a high-dimensional synthetic state estimation task and a 6D pose estimation derived from videos. We posit that as robotic systems evolve with greater degrees of freedom, particle filters tailored for high-dimensional state spaces will be indispensable.