Efficient image-based indoor localization with MEMS aid on the mobile device

Abstract

Recently, imaged-based localization has become a hotspot in indoor localization research due to its high accuracy and no additional infrastructure requirements. However, computational complexity and memory consumption are critical issues for terminal devices in natural large-scale indoor scenes. In this paper, we propose a system that takes advantage of built-in sensors, a microelectromechanical system (MEMS), and 3D model-based localization to overcome resource constraints. Furthermore, we propose a rough carrier six-degree-of-freedom (6-DoF) pose estimation and error analysis based on the pedestrian dead reckoning and attitude and heading reference systems (PDR/AHRS) with pedestrian motion constraints. We additionally recommend a geometric pose error propagation model to compute the 3D point pose uncertainty of corresponding 2D features on the query images and a matching pool to extract the activated points for determining the 2D-3D correspondence, which efficiently rejects the outlier through the geometric filter in the RANSAC loop of pose estimation. Moreover, based on the fusion of results from the MEMS-based and image-based localization, we can obtain an optimized carrier pose by using a dynamic reliability strategy to ensure the continuous robustness of the localization system and avoid failures when the matching lacks sufficient inliers. To evaluate the performance of the system, we conducted extensive experiments in large-scale indoor scenes. The results demonstrate that our image-based localization system generates excellent 2D-3D correspondence with the MEMS aid and that the proposed strategy obtains accurate 6-DoF poses in almost real time by using a smartphone. When compared to the conventional straightforward 2D-3D method (ACS) for localization accuracy and usability tests in various indoor scenes, the proposed method can save time by more than 80% and reduce translation and rotation errors by 27% and 10%, respectively. We also show that our new localization system has robust performance and stability for various indoor scenes and influences by different image resolutions and multiview gestures. Furthermore, our idea has a good application foreground, as 3D indoor data acquisition and pose estimation algorithms are compatible with the current state of data expansion for pedestrian navigation.