粒子フィルタの多様性維持のための祖先木クラスタリング
Ancestry Tree Clustering for Particle Filter Diversity Maintenance
粒子フィルタにおいて、粒子の祖先関係を表す木構造をクラスタリングすることで多様性を維持し、多峰性環境での早期収束を防ぐ手法を提案した。
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著者: Ilari Vallivaara, Bingnan Duan, Yinhuan Dong, Tughrul Arslan
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We propose a method for linear-time diversity maintenance in particle filtering. It clusters particles based on ancestry tree topology: closely related particles in sufficiently large subtrees are grouped together. The main idea is that the tree structure implicitly encodes similarity without the need for spatial or other domain-specific metrics. This approach, when combined with intra-cluster fitness sharing and the protection of particles not included in a cluster, effectively prevents premature convergence in multimodal environments while maintaining estimate compactness. We validate our approach in a multimodal robotics simulation and a real-world multimodal indoor environment. We compare the performance to several diversity maintenance algorithms from the literature, including Deterministic Resampling and Particle Gaussian Mixtures. Our algorithm achieves high success rates with little to no negative effect on compactness, showing particular robustness to different domains and challenging initial conditions.
関連論文
- 微分可能なリサンプリングに向けて粒子フィルタ
