Abstract

Location-based services (LBS) have significantly enhanced the convenience of mobile services for the general public, becoming an essential component of our daily lives and work. Accomplishing precise indoor positioning and implementing crowd-sourced mapping within multi-level parking structures presents a significant challenge. Indoor magnetic feature matching is a feasible solution but has to reduce the mapping cost by crowd-sourcing. To address the issue of crowd-sourced magnetic mapping in multi-level garages, we propose a novel solution for constructing crowd-sourced magnetic grid maps based on pedestrian-vehicle collaboration. This approach leverages the high-precision capabilities of Vehicle Dead Reckoning (VDR) to gather magnetic field data and floor information from the primary routes within parking structures, utilizing crowdsourced vehicle data. In processing vehicle crowd-sourced data, we employ a method akin to pedestrian trajectory processing, coupled with floor transition detection, to impose floor-level constraints. Through global trajectory optimization, we achieve precise estimation of 3D vehicle trajectories from the crowdsourced data. Furthermore, by iteratively refining the graph optimization problem, incorporating keyframe association details, floor information, and adjacent loop closure data, we successfully reconstruct the vehicle's 3D trajectory. Field tests demonstrate that the proposed method can achieve an average planar positioning error of 2.19 meters using only vehicle-mounted smartphone sensor data. The relative height error between two floors is a mere 0.08 meters.