In the last decade, consumer electronic devices such as smartphones, are packaged with small cameras, gyroscopes, and accelerometers, all sensors allowing autonomous deployment of aerial robots in GPS-denied environments. Our previous work [1], demonstrated the feasibility of using smartphones for autonomous flight. In many applications, there is a large interest to the use multiple autonomous aerial vehicles in a cooperative manner to speed up the operation of the mission. In this work, we present the first fully autonomous smartphone-based swarm of quadrotors. Multiple vehicles are able to plan safe trajectories avoiding inter-robot collisions, optimizing at the same time a given task and concurrently building in a cooperative manner a 3-D map of the environment. The sensing, sensor fusion, control, and planning are all done on an offthe- shelf Samsung Galaxy S5 smartphone using just the single camera and IMU available on the phone. The work allows any consumer with multiple smartphones to autonomously drive a swarm of multiple vehicles without GPS, by downloading an app, and have the swarm cooperatively map a 3-D environment.

Consumer grade technology seen in cameras and phones has led to the price/performance ratio of sensors and processors falling dramatically over the last decade. In particular, most devices are packaged with a camera, a gyroscope, and an accelerometer, important sensors for aerial robotics. The low mass and small form factor make them particularly well suited for autonomous flight with small flying robots, especially in GPS-denied environments. In this work, we present the first fully autonomous smartphone-based quadrotor. All the computation, sensing and control runs on an off-the-shelf smartphone, with all the software functionality in a smartphone app.We show how quadrotors can be stabilized and controlled to achieve autonomous flight in indoor buildings with application to smart homes, search and rescue, construction and architecture. The work allows any consumer with a smartphone to autonomously drive a quadrotor robot platform, even without GPS, by downloading an app, and concurrently build 3-D maps.

In this proposal we show case two 3D reconstruction systems running in real-time on a tablet equipped with a depth sensor. We believe that the proposed set of demonstrations will engage ISMAR attendees both in terms of tracking technology and user experience. Both demos show state-of-the art 3D reconstruction technology and give attendees a chance to try hands-on our tracking with simple and interactive user interfaces.

In this proposal we suggest a set of demonstrations to be presented at ISMAR 2014 around the topic of monocular tracking, mapping and rendering on mobile phones. We believe that the proposed set of demonstrations will engage ISMAR attendees both in terms of tracking technology and user experience. On the one hand, all demos show state-of-the art tracking and mapping technology and gives attendees a chance to try hands-on our tracking solutions and to discuss them with our researchers and developers. All demos also showcase compelling use cases enabled by our technology, encouraging discussions on topics such as integration of tracking technology with game engines and user experience research.

The development of new Simultaneous Localization and Mapping (SLAM) techniques is quickly advancing in research communities and rapidly transitioning into commercial products. Creating accurate and high-quality SLAM maps relies on a robust initialization process. However, the robustness and usability of SLAM initialization for end-users has often been disregarded. This paper presents and evaluates a novel tracking system for 6DOF pose tracking between a single keyframe and the current camera frame, without any prior scene knowledge. Our system is particularly suitable for SLAM initialization, since it allows 6DOF pose tracking in the intermediate frames before a wide-enough baseline between two keyframes has formed. We investigate how our tracking system can be used to interactively guide users in performing an optimal motion for SLAM initialization. However, our findings from a pilot study indicate that the need for such motion can be completely hidden from the user and outsourced to our tracking system. Results from a second user study show that letting our tracking system create a SLAM map as soon as possible is a viable and usable solution. Our work provides important insight for SLAM systems, showing how our novel tracking system can be integrated with a user interface to support fast, robust and user-friendly SLAM initialization.

Recently, there has been tremendous interest in the area of mobile Augmented Reality (AR) with applications including navigation, social networking, gaming and education. Current generation mobile phones are equipped with camera, GPS and other sensors, e.g., magnetic compass, accelerometer, gyro in addition to having ever increasing computing/graphics capabilities and memory storage. Mobile AR applications process the output of one or more sensors to augment the real world view with useful information. This paper's focus is on the camera sensor output, and describes the building blocks for a vision-based AR system. We present information-theoretic techniques to build and maintain an image (feature) database based on reference images, and for querying the captured input images against this database. Performance results using standard image sets are provided demonstrating superior recognition performance even with dramatic reductions in feature database size.

Academic/Research Overview of Mobile Augmented Reality Introduction to the Qualcomm AR Platform — Features and Usage Scenarios Developing Mobile AR Applications using Qualcomm's Unity Extension Introduction to Qualcomm's QCAR SDK Native API Cross Platform Development with the Native QCAR SDK