Particle filtering is a common technique for six degree 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：粒子滤波是一种常用的六自由度（6D）姿态估计技术，因其能够高效地表示目标姿态的置信分布。然而，由于6D姿态的高维特性，粒子滤波容易出现粒子匮乏问题。当粒子匮乏发生时，会引发重要性采样过程中底层置信分布的模态崩溃。若真实状态附近的区域发生模态崩溃，则恢复其置信变得困难，因为该区域不再被粒子形成的概率质量所覆盖。以往的方法通过随机化和重置置信分布中的粒子来缓解此问题，但重振频率的确定依赖于人工调整的抽象启发式规则。本文通过引入一种与标准似然函数协同使用的反假设似然函数，在每一时间步估计所需的重振率。受证据推理中“合理”与“不合理”概念的启发，反假设似然函数的加入为每个粒子分配了一个怀疑度。利用粒子集内置信与怀疑的累积竞争值，可估计滤波器内的失效程度，从而确定需要重振的粒子比例。我们在刚体目标6D姿态追踪任务上验证了该方法的有效性。