用祖先树结构聚类粒子，高效保持多样性并防早熟收敛。
Ancestry Tree Clustering for Particle Filter Diversity Maintenance
- 基于祖先树拓扑聚类，无需依赖空间度量
- 在多模态场景中成功率高，且估计紧凑性不受损
- 适合复杂环境下的机器人定位与跟踪任务
我们提出一种线性时间的粒子滤波多样性维持方法。该方法根据粒子的祖先树拓扑结构进行聚类：在足够大的子树中关系密切的粒子被归为一组。核心思想是树结构隐式编码了相似性，无需使用空间或其他领域特定度量。结合组内适应度共享及对未被聚类粒子的保护机制，该方法在多模态环境中有效防止早熟收敛，同时保持估计的紧凑性。我们在一个多模态机器人仿真环境和一个真实世界的多模态室内环境中验证了该方法的有效性。与文献中的多种多样性维持算法（包括确定性重采样和粒子高斯混合模型）相比，该算法在几乎不损害紧凑性的前提下实现高成功率，对不同领域和挑战性初始条件表现出显著鲁棒性。
原文摘要 · Abstract (English)
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.
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