Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps

Daniel Honerkamp, S Guttikonda, Abhinav Valada

Keywords: Localization, Active Perception, Reinforcement Learning

TL;DR: We propose an active localization method that couples learned particle filters with a reinforcement learning agent with selective attention which enables the method to scale to continuous actions and arbitray map sizes.

Abstract:

Accurate localization is a critical requirement for
most robotic tasks. The main body of existing work is focused
on passive localization in which the motions of the robot are
assumed given, abstracting from their influence on sampling
informative observations. While recent work has shown the
benefits of learning motions to disambiguate the robot’s poses,
these methods are restricted to granular discrete actions and
directly depend on the size of the global map. We propose Active
Particle Filter Networks (APFN), an approach that only relies
on local information for both the likelihood evaluation as well as
the decision making. To do so, we couple differentiable particle
filters with a reinforcement learning agent that attends to the
most relevant parts of the map. The resulting approach inherits
the computational benefits of particle filters and can directly act
in continuous action spaces while remaining fully differentiable
and thereby end-to-end optimizable as well as agnostic to the
input modality. We demonstrate the benefits of our approach
with extensive experiments in photorealistic indoor environ-
ments built from real-world 3D scanned apartments. Videos and
code are available at http://apfn.cs.uni-freiburg.de.