Combining Interactive Spatial Augmented Reality with HeadMounted Display for EndUser Collaborative Robot Programming This paper proposes an intuitive approach for collaborative robot enduser programming using a combination of interactive spatial augmented reality (ISAR) and headmounted display (HMD). It aims to reduce userxe2x80x99s workload and to let the user program the robot faster than in classical approaches (e.g. kinesthetic teaching). The proposed approach, where user is using a mixedreality HMD xe2x80x93 Microsoft HoloLens xe2x80x93 and touchenabled table with SAR projected interface as input devices, is compared to a baseline approach, where robotxe2x80x99s arms and a touchenabled table are used as input devices. Main advantages of the proposed approach are the possibility to program the collaborative workspace without the presence of the robot, its speed in comparison to the kinesthetic teaching and an ability to quickly visualize learned program instructions, in form of virtual objects, to enhance the usersxe2x80x99 orientation within those programs. The approach was evaluated on a set of 20 users using the withinsubject experiment design. Evaluation consisted of two pick and place tasks, where users had to start from the scratch as well as to update the existing program. Based on the experiment results, the proposed approach is better in qualitative measures by 33.84% and by 28.46% in quantitative measures over the baseline approach for both tasks.

Collaborative Human Augmented SLAM In this paper, we are proposing a collaborative SLAM system between a team of three heterogeneous agents: a robot, a human operator, and an augmented reality head mounted display (ARHMD). The system allows for online editing of a map produced by a robot running SLAM. Through hand gestures, the user can edit, in real time, the robot map that is augmented on top of the physical environment. Moreover, the proposed system leverages the builtin SLAM capabilities of the ARHMD to correct the robotxe2x80x99s map and map areas that are not yet discovered by the robot. Our method aims to combine the unique and complementary capabilities of each of the three different agents to produce the maximum possible mapping accuracy in the minimum amount of time. The proposed system is implemented on ROS and Unity. Experiments performed demonstrate the considerably superior SLAM outputs in terms of reducing mapping time, eliminating maps postprocessing, and increasing mapping accuracy.