Decentralized VisualInertial Localization and Mapping on Mobile Devices for Augmented Reality In this paper, we present a novel approach to shared augmented reality (AR) for mobile devices operating in the same area that does not rely on cloud computing. In particular, each userxe2x80x99s device processes the visual and inertial data received from its sensors and almost immediately broadcasts a partial feature map of its surroundings. In parallel, every device localizes against the broadcasted maps by employing feature observations to determine its 4DOF transformation to each of the gravityaligned maps. By doing so, virtual content placed by any of the users is quickly and accurately displayed in all other usersxe2x80x99 views. Furthermore, to reduce the effect of inconsistency introduced by relocalizing against incrementally created and updated maps, their transformations w.r.t. the device are modeled as random processes driven by white noise instead of constant, unknown parameters. Lastly, we assess the accuracy of our approach for the case of two users in a roomscale environment against VICON groundtruth.

Collaborative Human Augmented SLAM In this paper, we are proposing a collaborative SLAM system between a team of three heterogeneous agents: a robot, a human operator, and an augmented reality head mounted display (ARHMD). The system allows for online editing of a map produced by a robot running SLAM. Through hand gestures, the user can edit, in real time, the robot map that is augmented on top of the physical environment. Moreover, the proposed system leverages the builtin SLAM capabilities of the ARHMD to correct the robotxe2x80x99s map and map areas that are not yet discovered by the robot. Our method aims to combine the unique and complementary capabilities of each of the three different agents to produce the maximum possible mapping accuracy in the minimum amount of time. The proposed system is implemented on ROS and Unity. Experiments performed demonstrate the considerably superior SLAM outputs in terms of reducing mapping time, eliminating maps postprocessing, and increasing mapping accuracy.