  Object pose estimation is a crucial prerequisite for robots to perform
autonomous manipulation in clutter. Real-world bin-picking settings such as
warehouses present additional challenges, e.g., new objects are added
constantly. Most of the existing object pose estimation methods assume that 3D
models of the objects is available beforehand. We present a pipeline that
requires minimal human intervention and circumvents the reliance on the
availability of 3D models by a fast data acquisition method and a synthetic
data generation procedure. This work builds on previous work on semantic
segmentation of cluttered bin-picking scenes to isolate individual objects in
clutter. An additional network is trained on synthetic scenes to estimate
object poses from a cropped object-centered encoding extracted from the
segmentation results. The proposed method is evaluated on a synthetic
validation dataset and cluttered real-world scenes.
