Counter-Hypothetical Particle Filters for Single Object Pose Tracking

Elizabeth Olson, Jana Pavlasek, Jasmine Berry, Odest Jenkins

Keywords: 6D single object pose tracking, particle filters, particle reinvigoration

TL;DR: We train a network to estimate the level of doubt associated with each particle, which is used to determine the amount of particle reinvigoration necessary.

Abstract: Particle filtering is a common technique for six degree of freedom (6D) pose estimation because of its ability to tractably represent belief over object pose. Due to the high-dimensional nature of 6D pose, this application of the particle filter is prone to particle deprivation and can lead to mode collapse of the underlying belief distribution in the importance sampling step. When this occurs, recovering belief in the region surrounding the true state is challenging since it is no longer represented in the probability mass formed by the particles. Previous methods mitigate this problem by reinvigorating particles in the predicted belief, 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.