Proof of concept of a projection-based safety system for human-robot collaborative engine assembly

In the past years human-robot collaboration has gained interest among industry and production environments. While there is interest towards the topic, there is a lack of industrially relevant cases utilizing novel methods and technologies. The feasibility of the implementation, worker safety and production efficiency are the key questions in the field. The aim of the proposed work is to provide a conceptual safety system for context-dependent, multi-modal communication in human-robot collaborative assembly, which will contribute to safety and efficiency of the collaboration. The approach we propose offers an addition to traditional interfaces like push buttons installed at fixed locations. We demonstrate an approach and corresponding technical implementation of the system with projected safety zones based on the dynamically updated depth map and a graphical user interface (GUI). The proposed interaction is a simplified two-way communication between human and the robot to allow both parties to notify each other, and for the human to coordinate the operations.acceptedVersionPeer reviewe

Robustifying correspondence based 6D object pose estimation

We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improves robustness. The second method, region pruning, is more general by making no assumptions about local surface properties. Our region pruning segments a model point cloud into cluster regions and searches good region combinations using a validation set. The robustifying methods are general and can be used with any correspondence based method. For the experiments, we evaluated three correspondence selection methods, Geometric Consistency (GC) [1], Hough Grouping (HG) [2] and Search of Inliers (SI) [3] and report systematic improvements for their robustified versions with two distinct datasets.acceptedVersionPeer reviewe