  Having a precise and robust transformation between the robot coordinate
system and the AR-device coordinate system is paramount during human-robot
interaction (HRI) based on augmented reality using Head mounted displays (HMD),
both for intuitive information display and for the tracking of human motions.
Most current solutions in this area rely either on the tracking of visual
markers, e.g. QR codes, or on manual referencing, both of which provide
unsatisfying results. Meanwhile a plethora of object detection and referencing
methods exist in the wider robotic and machine vision communities. The
precision of the referencing is likewise almost never measured. Here we would
like to address this issue by firstly presenting an overview of currently used
referencing methods between robots and HMDs. This is followed by a brief
overview of object detection and referencing methods used in the field of
robotics. Based on these methods we suggest three classes of referencing
algorithms we intend to pursue - semi-automatic, on-shot; automatic, one-shot;
and automatic continuous. We describe the general workflows of these three
classes as well as describing our proposed algorithms in each of these classes.
Finally we present the first experimental results of a semi-automatic
referencing algorithm, tested on an industrial KUKA KR-5 manipulator.
