Tracking errors severely impact the effectiveness of augmented reality display techniques for indoor navigation. In this work we take a look at the sources of error and accuracy of existing tracking technologies. We derive important design criteria for robust display techniques and present objective criteria. These serve evaluation of indoor navigation techniques without or in preparation of quantitative user studies. Based on these criteria we propose a new error tolerant display technique called Bending Words, where words move along the navigation path guiding the user. Bending Words outranks the other evaluated display techniques in many of the tested criteria and provides a robust, error-tolerant alternative to established augmented reality indoor navigation display techniques.

Simultaneous Localization and Mapping aims to identify the current position of an agent and to map his surroundings at the same time. Visual inertial SLAM algorithms use input from visual and motion sensors for this task. Since modern smartphones are equipped with both needed sensors, using VI-SLAM applications becomes feasible, with Augmented Reality being one of the most promising application areas. Android, having the largest market share of all mobile operating systems, is of special interest as the target platform. For iOS there already exists a high-quality open source implementation for VI-SLAM: The framework VINS-Mobile. In this work we discuss what steps are necessary for porting it to the Android operating system. We provide a practical guide to the main challenge: The correct calibration of device specific parameters for any Android smartphone. We present our results using the Samsung Galaxy S7 and show further improvement possibilities.