RESUMEN
Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.