Nowadays the application of AR is expanding from small or medium environments to large-scale environments, where the visual-based localization in the large-scale environments becomes a critical demand. Current visual-based localization techniques face robustness challenges in complex large-scale environments, requiring tremendous number of data with groundtruth localization for algorithm benchmarking or model training. The previous groundtruth solutions can only be used outdoors, or require high equipment/labor costs, so they cannot be scalable to large environments for both indoors and outdoors, nor can they produce large amounts of data at a feasible cost. In this work, we propose LSFB, a novel low-cost and scalable framework to build localization benchmark in large-scale indoor and outdoor environments. The key is to reconstruct an accurate HD map of the environment. For each visual-inertial sequence captured in the environment, the groundtruth poses are obtained by joint optimization taking both the HD map and visual-inertial constraints. The experiments demonstrate the obtained groundtruth poses have cm-level accuracy. We use the proposed method to collect a localization dataset by mobile phones and AR glasses in various environments with various motions, and release the dataset as the first large-scale localization benchmark for AR.

Accurate localization of a display device is essential for AR in large-scale environments. Visual-based localization is the most commonly used solution, but poses privacy risks, suffers from robustness issues and consumes high power. Wireless signal-based localization is a potential visual-free solution, but its accuracy is not enough for AR. In this paper, we present MagLoc-AR, a novel visual-free localization solution that achieves sufficient accuracy for some AR applications (e.g. AR navigation) in large-scale indoor environments. We exploit the location-dependent magnetic field interference that is ubiquitous indoors as a localization signal. Our method requires only a consumer-grade 9-axis IMU, with the gyroscope and acceleration measurements used to recover the motion trajectory, and the magnetic measurements used to register the trajectory to the global map. To meet the accuracy requirement of AR, we propose a mapping method to reconstruct a globally consistent magnetic field of the environment, and a localization method fusing the biased magnetic measurements with the network-predicted motion to improve localization accuracy. In addition, we provide the first dataset for both visual-based and geomagnetic-based localization in large-scale indoor environments. Evaluations on the dataset demonstrate that our proposed method is sufficiently accurate for AR navigation and has advantages over the visual-based methods in terms of power consumption and robustness. Project page: https://github.com/zju3dv/MagLoc-AR/.