Particle filtering is a common technique for six degrees of freedom (6D) pose estimation due to its ability to tractably represent belief over object pose. However, the particle filter is prone to particle deprivation due to the high-dimensional nature of 6D pose. When particle deprivation occurs, it can cause mode collapse of the underlying belief distribution during importance sampling. If the region surrounding the true state suffers from mode collapse, recovering its belief is challenging since the area is no longer represented in the probability mass formed by the particles. Previous methods mitigate this problem by randomizing and resetting particles in the belief distribution, but determining the frequency of reinvigoration has relied on hand-tuning abstract heuristics. In this paper, we estimate the necessary reinvigoration rate at each time step by introducing a Counter-Hypothetical likelihood function, which is used alongside the standard likelihood. Inspired by the notions of plausibility and implausibility from Evidential Reasoning, the addition of our Counter-Hypothetical likelihood function assigns a level of doubt to each particle. The competing cumulative values of confidence and doubt across the particle set are used to estimate the level of failure within the filter, in order to determine the portion of particles to be reinvigorated. We demonstrate the effectiveness of our method on the rigid body object 6D pose tracking task.

翻译：粒子滤波因其能够可处理地表示目标姿态的置信度，成为六自由度姿态估计的常用技术。然而，由于六自由度姿态的高维特性，粒子滤波器容易遭受粒子贫乏问题。当粒子贫乏发生时，它会在重要性采样过程中导致底层置信分布发生模态坍缩。如果真实状态周围的区域发生模态坍缩，恢复其置信度将变得困难，因为该区域不再由粒子形成的概率质量所表征。先前的方法通过在置信分布中随机化和重置粒子来缓解此问题，但确定粒子重激活的频率一直依赖于手动调整的抽象启发式规则。本文通过引入一个反假设似然函数（与标准似然函数一同使用），在每一时间步估计必要的重激活率。受证据推理中"合理性"与"不合理性"概念的启发，我们增加的反假设似然函数为每个粒子分配一个怀疑度。粒子集中置信度与怀疑度的竞争性累积值被用于估计滤波器内部的失效程度，从而确定需要重激活的粒子比例。我们在刚体目标六自由度姿态跟踪任务上验证了所提方法的有效性。