Augmented reality

About: Augmented reality is a research topic. Over the lifetime, 36039 publications have been published within this topic receiving 479617 citations. The topic is also known as: AR.

Realistic occlusion for a head mounted augmented reality display

Abstract: Technology is described for providing realistic occlusion between a virtual object displayed by a head mounted, augmented reality display system and a real object visible to the user's eyes through the display. A spatial occlusion in a user field of view of the display is typically a three dimensional occlusion determined based on a three dimensional space mapping of real and virtual objects. An occlusion interface between a real object and a virtual object can be modeled at a level of detail determined based on criteria such as distance within the field of view, display size or position with respect to a point of gaze. Technology is also described for providing three dimensional audio occlusion based on an occlusion between a real object and a virtual object in the user environment.

Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design:

Abstract: NASA's vision for space exploration stresses the cultivation of human-robotic systems. Similar systems are also envisaged for a variety of hazardous earthbound applications such as urban search and rescue. Recent research has pointed out that to reduce human workload, costs, fatigue driven error and risk, intelligent robotic systems will need to be a significant part of mission design. However, little attention has been paid to joint human-robot teams. Making human-robot collaboration natural and efficient is crucial. In particular, grounding, situational awareness, a common frame of reference and spatial referencing are vital in effective communication and collaboration. Augmented Reality (AR), the overlaying of computer graphics onto the real worldview, can provide the necessary means for a human-robotic system to fulfill these requirements for effective collaboration. This article reviews the field of human-robot interaction and augmented reality, investigates the potential avenues for creating natural...

YouMove: enhancing movement training with an augmented reality mirror

Abstract: YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.

Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds

Abstract: Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-Region-Proposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTI-classes, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.