APPEAR: Adaptive Pose Estimation for Mobile Augmented Reality

In Mobile Augmented Reality (MAR) applications, rendering virtual objects accurately on the user’s screen relies on knowing the poses of the user and the virtual objects. Typically, Simultaneous Localization and Mapping (SLAM) is used for pose estimation, but SLAM consumes lots of space and time. Many MAR applications such as gaming and navigation do not need continuous mapping, hence Visual Odometry (VO) is developed that provides localization without map generation. This paper evaluates VO’s accuracy compared to SLAM by specifically considering feature points at varying depths which distinguishes indoor and outdoor environments. Our findings suggest VO performs better with distant feature points (i.e., commonly outdoors), while SLAM excels with closer feature points (i.e., typically indoors). These findings inspire us to design APPEAR, an adaptive approach that switches between VO and SLAM as needed. We implemented and evaluated APPEAR on a computer rather than a smartphone because the chosen VO method was not available on a phone. APPEAR demonstrates memory savings of up to 100 MB and a 1.67 x speed boost on medium-sized datasets without losing accuracy. Additionally, while Absolute Trajectory Error (ATE) has been the primary metric for evaluating pose estimation, we argue for more comprehensive quality metrics for MAR applications: accuracy, precision, and recall. We also define selective ATE where ATE is only calculated in the region where the virtual objects are visible.