Robert Huitl

TL;DR: This work proposes a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices to combine relative motion information based on step detection with WiFi signal strength measurements based on the well-known particle filter methodology.

TL;DR: Video recordings of a mobile device as a visual fingerprint of the environment and matching them to a georeferenced database provides pose information in a very natural way and can be provided without complex infrastructure in areas where the accuracy and availability of GPS is limited.

TL;DR: An extensive, high resolution indoor dataset that includes realistic query sequences with ground truth as well as point cloud data, enabling a localization system to perform 6-DOF pose estimation.

TL;DR: This work presents a combined interface of Virtual Reality (VR) and Augmented Reality (AR) elements with indicators that help to communicate and ensure localization accuracy and found that AR was preferred in case of reliable localization, but with VR, navigation instructions were perceived more accurate in cases of localization and orientation errors.

TL;DR: An indoor localization system, which allows instantaneous camera-based indoor positioning with very low requirements on the available network connection, and a scalable streaming approach that preemptively loads image signatures of reference images in the vicinity of the user onto the mobile device to mitigate the effect of network latency.