  We present an unsupervised framework for simultaneous appearance-based object
discovery, detection, tracking and reconstruction using RGBD cameras and a
robot manipulator. The system performs dense 3D simultaneous localization and
mapping concurrently with unsupervised object discovery. Putative objects that
are spatially and visually coherent are manipulated by the robot to gain
additional motion-cues. The robot uses appearance alone, followed by structure
and motion cues, to jointly discover, verify, learn and improve models of
objects. Induced motion segmentation reinforces learned models which are
represented implicitly as 2D and 3D level sets to capture both shape and
appearance. We compare three different approaches for appearance-based object
discovery and find that a novel form of spatio-temporal super-pixels gives the
highest quality candidate object models in terms of precision and recall. Live
experiments with a Baxter robot demonstrate a holistic pipeline capable of
automatic discovery, verification, detection, tracking and reconstruction of
unknown objects.
