We present an off-the-shelf, low-latency Optical See-through Head-Mounted Displays (OST-HMD) for Augmented Reality (AR). Temporally consistent visualization is crucial for realizing immersive AR experiences. This is challenging since it requires both accurate head-tracking and low-latency rendering of AR content. Building a system which meets both constraints usually requires experts on computer vision/graphics and expensive display hardware. This work demonstrates that such high spatio-temporal fidelity is achievable with commodity hardware available today. We build a custom OST-HMD system that consists of a virtual reality HMD, i.e., the Oculus Rift DK2, and half-mirror optics, and adapt the rendering pipeline in order to integrate the OST-HMD calibration framework. An evaluation with a user-perspective camera shows that the system achieves mean temporal error of <;1 ms (95% reduction of the latency from naive, no-predictive rendering), and median spatial error <;0.3° in the viewing angle with maximum error at most 1.0°.

Marker-based optical outside-in tracking is a mature and robust technology used by many AR, VR and motion capture applications. However, in small environments the tracking cameras are often difficult to install. An example scenario are ergonomic studies in car manufacturing, where the motion of a worker needs to be tracked in small spaces such as the trunk of a car. In this paper, we describe how to extend the tracking volume in small, cluttered environments using small and flexible wireless cameras in form of unmodified mobile phones that can quickly be installed. Since those small cameras are not synchronized with the main tracking cameras, we describe several modifications to the tracking algorithms, such as inter-frame interpolation, the replacement of the least-squares adjustment by a Kalman filter and the integration of rolling-shutter compensation. To support the quick setup of additional cameras while the tracking system is running, the system is extended by an on-line calibration technique that determines the extrinsic camera parameters without requiring a dedicated calibration step.

We describe the design and implementation of a physical and virtual model of an imaginary urban scene-the “City of Sights”- that can serve as a backdrop or “stage” for a variety of Augmented Reality (AR) research. We argue that the AR research community would benefit from such a standard model dataset which can be used for evaluation of such AR topics as tracking systems, modeling, spatial AR, rendering tests, collaborative AR and user interface design. By openly sharing the digital blueprints and assembly instructions for our models, we allow the proposed set to be physically replicable by anyone and permit customization and experimental changes to the stage design which enable comprehensive exploration of algorithms and methods. Furthermore we provide an accompanying rich dataset consisting of video sequences under varying conditions with ground truth camera pose. We employed three different ground truth acquisition methods to support a broad range of use cases. The goal of our design is to enable and improve the replicability and evaluation of future augmented reality research.

Indoor tracking scenarios still face challenges in providing continuous tracking support in wide-area workplaces. This is especially the case in Augmented Reality since such augmentations generally require exact full 6DOF pose measurements in order to continuously display 3D graphics from user-related view points. Many single sensor systems have been explored but only few of them have the capability to track reliably in wide-area environments. We introduce a mobile multi-sensor platform to overcome the shortcomings of single sensor systems. The platform is equipped with a detachable optical camera and a rigidly mounted odometric measurement system providing relative positions and orientations with respect to the ground plane. The camera is used for marker-based as well as for marker-less (feature-based) inside-out tracking as part of a hybrid approach. We explain the principle tracking technologies in our competitive/cooperative fusion approach and show possible enhancements to further developments. This inside-out approach scales well with increasing tracking range, as opposed to stationary outside-in tracking.

Spatial tracking is one of the most challenging parts of Augmented Reality. Many AR applications rely on the fusion of several tracking systems in order to optimize the overall performance. While the topic of sensor fusion has already seen considerable interest, most results only deal with the integration of particular setups. A crucial part of sensor fusion is the temporal alignment of the sensor signals, as sensors in general are not synchronized. We present a general method to calibrate the temporal offset between different sensors by applying the normalized cross correlation method.

For a mixed reality user the available tracking quality usually is a balance between technical requirements, user needs and economic factors, as most tracking methodologies require setup of dedicated infrastructure. As pervasive and ubiquitous computing grow in importance, so does the associated information infrastructure. Combining these two facts we present strategies to leverage existing information infrastructure and exploit its characteristics to empower users with services to determine their position, without the need for dedicated infrastructure.

Many types of tangible interaction systems, such as interactive surfaces and gesture-based interfaces, are based on various kinds of optical tracking, using infrared illuminators and cameras. One drawback of these setups is that they suffer from problems common to optical trackers, such as sensitivity to stray environment light from artificial and natural sources. In this paper, we present a method to significantly enhance tracking robustness for those systems which employ active illumination. Through addition of a small electronic circuit which modulates the LEDs used to illuminate the scene, contrast can be significantly increased.

Multi-touch interfaces have been a focus of research in recent years, resulting in development of various innovative UI concepts. Support for existing WIMP interfaces, however, should not be overlooked. Although several approaches exist, there is still room for improvement, particularly regarding implementation of the "hover" state, commonly used in mouse-based interfaces.

It is widely recognized that inertial sensors, in particular gyroscopes, can improve the latency and accuracy of orientation tracking by fusing the inertial measurements with data from other sensors. In our previous work, we introduced the concepts of spatial relationship graphs and spatial relationship patterns to formally model multi-sensor tracking setups and derive valid applications of well-known algorithms in order to infer new spatial relationships for tracking and calibration. In this work, we extend our approach by providing additional spatial relationship patterns that transform incremental rotations and add gyroscope alignment and fusion. The usefulness of the resulting tracking configurations is evaluated in two different scenarios with both inside-out and outside-in tracking.

Ubiquitous tracking setups, covering large tracking areas with many heterogeneous sensors of varying accuracy, require dedicated middleware to facilitate development of stationary and mobile applications by providing a simple interface and encapsulating the details of sensing, calibration and sensor fusion. In this paper we present a centrally coordinated peer-to-peer architecture for ubiquitous tracking, where a server computes optimal data flow configurations for sensor and application clients, which are directly exchanging tracking data with low latency using a light-weight data flow framework. The server's decisions are inferred from an actively maintained central spatial relationship graph (SRG) using spatial relationship patterns. The system is compared to a previous Ubitrack implementation using the highly distributed DWARF middleware. It exhibits significantly better performance in a reference scenario.