OGI-SLAM2: A Hybrid Map SLAM Framework Grounded in Inertial-Based SLAM

Indoor localization using mobile sensors has gained momentum lately. However, indoor pedestrian localization with low-cost inertial measurement unit (IMU) has not been practically applied, and the main challenge is that it is difficult for low-cost IMU to obtain high-precision localization performance over a long period of time. In this article, we propose a multisource fusion indoor pedestrian autonomous localization framework combining landmark maps and occupancy grid maps. Combining the ideas of optimization and occupancy grid map-based inertial simultaneous localization and mapping (OGI-SLAM) algorithm, we maintain the short- and medium-term localization accuracy with OGI-SLAM and achieve long-term localization accuracy improvement with keypoint maps. Without prior knowledge of the map, we use user motion features and environmental features to extract keypoints in the trajectory and construct keypoint descriptors. Combining the uncertainty of system location and the similarity of environmental features, we propose a keypoint extraction and association method to solve the problem of inaccurate particle filtering loop closing with low-cost IMU. In addition, a two-step trajectory optimization method is proposed, which uses bundle adjustment to correct the positioning error of keypoints and models the error correction of common points between keypoints as affine transformation, avoiding the large matrix inversion operation in the optimization process. Simulations and field trial evaluations show that the system is able to achieve a maximum positioning accuracy of 1.64 m with low-performance IMU on board, representing a 57% maximum improvement in positioning accuracy compared to OGI-SLAM. This highlights the promise of OGI-SLAM2 as an unconventional indoor positioning method.