A Lightweight 6-Dof Pose Serialization for Outdoor Visual-Inertial Ar Pedestrian Navigation

Nowadays, pedestrian navigation has increased in popularity with the widespread use of smartphones among people. However, its visual-inertial location-based augmented reality (LAR) application is often constrained by heavy environmental data payloads, such as image features, point clouds, or 3D meshes for localization and AR object alignment. These data-intensive structures introduce high latency, large storage demands, and poor runtime performance on mobile devices. In this paper, to address these challenges, we present a lightweight data representation technique designed for LAR pedestrian navigation scenarios. The proposed method decouples AR navigation contents from the complex spatial maps. It stores a single cloud anchor persistence identifier and a minimal list of relative six-degree-of-freedom (6 DoF) poses in the AR object database system. For evaluations, we compared the proposal with a conventional cloud-based LAR implementation using Android Studio Profiler, focusing on render success rate, CPU load, memory usage, and network payload size. The results show that our approach lowered CPU utilization by up to 32%, memory usage by up to 25%, and reduced network data load by more than 90%, demonstrating its potential for scalable, low-latency, and persistent AR experiences on mobile devices.