wMPS-SLAM: An Online and Accurate Monocular Visual-wMPS SLAM System

Tracking drift issues resulting from the lack of global positioning constraints prevent visual simultaneous localization and mapping (SLAM) systems from providing accurate pose estimation services, especially in indoor environments. Similar to the function of the Global Navigation Satellite System (GNSS), a new indoor equipment called workshop measurement positioning system (wMPS) has been developed to provide millimeter-level global positioning information in the large-volume metrology industrial community but is rarely applied to SLAM solutions. In this article, we introduce a novel wMPS-SLAM framework that combines 3-degree-of-freedom (DoF) wMPS signals into our monocular pose estimation algorithm, enabling drift-free global 6-DoF pose estimation in indoor scenarios. First, a system calibration method is proposed to establish the relationship between the camera and wMPS and fuse the sensor information. Second, a variant similarity transformation scheme is employed to jointly estimate the scale factor and extrinsic parameters of the camera frame with respect to the world frame for estimating the global camera poses. Finally, a pose graph optimization method that minimizes global position and relative pose error (RPE) is utilized to fuse visual odometry (VO) estimation and wMPS measurements. Evaluation experiments conducted on two datasets demonstrate that wMPS-SLAM outperforms the state-of-the-art monocular visual solution (ORB-SLAM3) in terms of accuracy. Specifically, the absolute translation error (ATE) of the proposed system is reduced from 28.58 to 7.018 mm in real-world experiments.