  Introducing semantically meaningful objects to visual Simultaneous
Localization And Mapping (SLAM) has the potential to improve both the accuracy
and reliability of pose estimates, especially in challenging scenarios with
significant view-point and appearance changes. However, how semantic objects
should be represented for an efficient inclusion in optimization-based SLAM
frameworks is still an open question. Superquadrics(SQs) are an efficient and
compact object representation, able to represent most common object types to a
high degree, and typically retrieved from 3D point-cloud data. However,
accurate 3D point-cloud data might not be available in all applications. Recent
advancements in machine learning enabled robust object recognition and semantic
mask measurements from camera images under many different appearance
conditions. We propose a pipeline to leverage such semantic mask measurements
to fit SQ parameters to multi-view camera observations using a multi-stage
initialization and optimization procedure. We demonstrate the system's ability
to retrieve randomly generated SQ parameters from multi-view mask observations
in preliminary simulation experiments and evaluate different initialization
stages and cost functions.
