  The demands on robotic manipulation skills to perform challenging tasks have
drastically increased in recent times. To perform these tasks with dexterity,
robots require perception tools to understand the scene and extract useful
information that transforms to robot control inputs. To this end, recent
research has introduced various object pose estimation and grasp pose detection
methods that yield precise results. Assembly pose estimation is a secondary yet
highly desirable skill in robotic assembling as it requires more detailed
information on object placement as compared to bin picking and pick-and-place
tasks. However, it has been often overlooked in research due to the complexity
of integration in an agile framework. To address this issue, we propose an
assembly pose estimation method with RGB-D input and 3D CAD models of the
associated objects. The framework consists of semantic segmentation of the
scene and registering point clouds of local surfaces against target point
clouds derived from CAD models to estimate 6D poses. We show that our method
can deliver sufficient accuracy for assembling object assemblies using
evaluation metrics and demonstrations. The source code and dataset for the work
can be found at: https://github.com/KulunuOS/6DAPose
