Counter-Hypothetical Particle Filters for Single Object Pose Tracking
Elizabeth A. Olson Jana Pavlasek Jasmine A. Berry Odest Chadwicke Jenkins
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.