Title: Simultaneous Localization and Layout Model Selection in Manhattan Worlds,
Abstract: In this paper, we will demonstrate how Manhattan structure can be exploited
to transform the Simultaneous Localization and Mapping (SLAM) problem, which is
typically solved by a nonlinear optimization over feature positions, into a
model selection problem solved by a convex optimization over higher order
layout structures, namely walls, floors, and ceilings. Furthermore, we show how
our novel formulation leads to an optimization procedure that automatically
performs data association and loop closure and which ultimately produces the
simplest model of the environment that is consistent with the available
measurements. We verify our method on real world data sets collected with
various sensing modalities.