Mixed reality (MR) registers virtual information and real objects and is an effective way to supplement astronaut training. Spatial anchors are generally used to perform virtual-real fusion in static scenes but cannot handle movable objects. To address this issue, we propose a smart task assistance method based on object detection and point cloud alignment. Specifically, both fixed and movable objects are detected automatically. In parallel, poses are estimated with no dependence on preset spatial position information. Firstly, YOLOv5s is used to detect the object and segment the point cloud of the corresponding structure, called the partial point cloud. Then, an iterative closest point (ICP) algorithm between the partial point cloud and the template point cloud is used to calculate the object's pose and execute the virtual-real fusion. The results demonstrate that the proposed method achieves automatic pose estimation for both fixed and movable objects without background information and preset spatial anchors. Most volunteers reported that our approach was practical, and it thus expands the application of astronaut training.
Keywords: astronaut training; mixed reality; object detection; point cloud alignment; pose estimation.