Mixed Reality-Based 6D-Pose Annotation System for Robot Manipulation in Retail Environments.

Robot manipulation in retail environments is a challenging task due to the need for large amounts of annotated data for accurate 6D-pose estimation of items. Onsite data collection, additional manual annotation, and model fine-tuning are often required when deploying robots in new environments, as varying lighting conditions, clutter, and occlusions can significantly diminish performance. Therefore, we propose a system to annotate the 6D pose of items using mixed reality (MR) to enhance the robustness of robot manipulation in retail environments. Our main contribution is a system that can display 6D-pose estimation results of a trained model from multiple perspectives in MR, and enable onsite (re-)annotation of incorrectly inferred item poses using hand gestures. The proposed system is compared to a PC-based annotation system using a mouse and the robot camera's point cloud in an extensive quantitative experiment. Our experimental results indicate that MR can increase the accuracy of pose annotation, especially by reducing position errors.