A common goal of outdoor augmented reality (AR) is the presentation of annotations that are registered to anchor points in the real world. We present an enhanced approach for registering and tracking such anchor points, which is suitable for current generation mobile phones and can also successfully deal with the wide variety of viewing conditions encountered in real life outdoor use. The approach is based on on-the-fly generation of panoramic images by sweeping the camera over the scene. The panoramas are then used for stable orientation tracking, while the user is performing only rotational movements. This basic approach is improved by several new techniques for the re-detection and tracking of anchor points. For the re-detection, specifically after temporal variations, we first compute a panoramic image with extended dynamic range, which can better represent varying illumination conditions. The panorama is then searched for known anchor points, while orientation tracking continues uninterrupted. We then use information from an internal orientation sensor to prime an active search scheme for the anchor points, which improves matching results. Finally, global consistency is enhanced by statistical estimation of a global rotation that minimizes the overall position error of anchor points when transforming them from the source panorama in which they were created, to the current view represented by a new panorama. Once the anchor points are redetected, we track the user's movement using a novel 3-degree-of-freedom orientation tracking approach that combines vision tracking with the absolute orientation from inertial and magnetic sensors. We tested our system using an AR campus guide as an example application and provide detailed results for our approach using an off-the-shelf smartphone. Results show that the re-detection rate is improved by a factor of 2 compared to previous work and reaches almost 90% for a wide variety of test cases while still keeping the ability to run at interactive frame rates.

Localization of mobile devices is an essential task in Augmented Reality and has therefore been an active research topic for many years. Typically, indoor tracking approaches, such as methods based on infrared or ultra-wide-band, require preparations of the environment and special hardware sensors. Conversely, image feature tracking approaches can provide orientation estimates without special tracking hardware installations. With the advent of mobile devices equipped with sensors such as digital cameras, image based localization gains importance in Augmented Reality. Typically, fiducial marker tracking was considered as a standard image based localization method. We propose the use of natural image feature based tracking methods, which are a generalization of the same principals but do not require the presence of fiducial tracking targets.

Magnetic compasses and accelerometers provide absolute orientation measurements within the earth's reference frame. However, sensor output typically suffers from jitter and external disturbances. Conversely, visual tracking provides more stable orientation estimation relative to an unknown initial orientation rather than to true north. We propose a 3-degree-of-freedom orientation tracking approach combining the accuracy and stability of vision tracking with the absolute orientation from inertial and magnetic sensors by estimating the offset between the initial orientation of the vision tracker and true north. We demonstrate that the approach improves absolute orientation estimation on a mobile phone device.

Outdoor Augmented Reality typically requires tracking in unprepared environments. For global registration, Global Positioning System (GPS) is currently the best sensing technology, but its precision and update rate are not sufficient for high quality tracking. We present a system that uses Kalman filtering for fusion of Differential GPS (DGPS) or Real-Time Kinematic (RTK) based GPS with barometric heights and also for an inertial measurement unit with gyroscopes, magnetometers and accelerometers to improve the transient oscillation. Typically, inertial sensors are subjected to drift and magnetometer measurements are distorted by electro-magnetic fields in the environment. For compensation, we additionally apply a visual orientation tracker which is drift-free through online mapping of the unknown environment. This tracker allows for correction of distortions of the 3-axis magnetic compass, which increases the robustness and accuracy of the pose estimates. We present results of applying this approach in an industrial application scenario.

Field workers of utility companies are regularly engaged in outdoor tasks such as network planning and inspection of underground infrastructure. Redlining is the term used for manually annotating either printed paper maps or a 2D geographic information system on a notebook computer taken to the field. Either of these approaches requires finding the physical location to be annotated on the physical or digital map. In this paper, we describe a mobile Augmented Reality (AR) system capable of supporting virtual redlining. The AR visualization delivered by the system is constructed from data directly extracted from a GIS used in day-to-day production by utility companies. We also report on encouraging trials and interviews performed with professional field workers from the utility sector.