Incremental Distributed Robust Inference from Arbitrary Robot Poses via EM and Model Selection
Abstract
We present a novel approach for multi-robot distributed
and incremental inference over variables of interest,
such as robot trajectories, considering the initial relative poses
between the robots and multi-robot data association are both
unknown. Assuming robots share with each other informative
observations, this inference problem is formulated within an
Expectation-Maximization (EM) optimization, performed by each
robot separately, alternating between inference over variables
of interest and multi-robot data association. To facilitate this
process, a common reference frame between the robots should
first be established. We show the latter is coupled with determining
multi-robot data association, and therefore concurrently
infer both using a separate EM optimization. This optimization is
performed by each robot starting from several promising initial
solutions, converging to locally-optimal hypotheses regarding
data association and reference frame transformation. Choosing
the best hypothesis in an incremental problem setting is in particular
challenging due to high sensitivity to perceptual aliasing
and possibly insufficient amount of data. Selecting an incorrect
hypothesis introduces outliers and can lead to catastrophic
results. To address these challenges we develop a model-selection
based approach to choose the most probable hypothesis and use
the Chinese restaurant process to disambiguate the hypotheses
prior probabilities over time.