Title: A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM,
Abstract: We present a Bayesian object observation model for complete probabilistic
semantic SLAM. Recent studies on object detection and feature extraction have
become important for scene understanding and 3D mapping. However, 3D shape of
the object is too complex to formulate the probabilistic observation model;
therefore, performing the Bayesian inference of the object-oriented features as
well as their pose is less considered. Besides, when the robot equipped with an
RGB mono camera only observes the projected single view of an object, a
significant amount of the 3D shape information is abandoned. Due to these
limitations, semantic SLAM and viewpoint-independent loop closure using
volumetric 3D object shape is challenging. In order to enable the complete
formulation of probabilistic semantic SLAM, we approximate the observation
model of a 3D object with a tractable distribution. We also estimate the
variational likelihood from the 2D image of the object to exploit its observed
single view. In order to evaluate the proposed method, we perform pose and
feature estimation, and demonstrate that the automatic loop closure works
seamlessly without additional loop detector in various environments.