DLIRO: Dual-Antenna RTK-Aided LiDAR-Inertial Odometry for Large-Scale Outdoor Localization

Abstract

High-accuracy outdoor SLAM often benefits from the fusion of RTK, LiDAR, and IMU, which combines global positioning capability with robust local geometric and inertial constraints. However, many existing methods mainly rely on singleantenna RTK or use GNSS primarily as position-level constraints, while the additional heading observability provided by dual-antenna RTK remains underexplored. To address this issue, we propose a tightly coupled GNSS–LiDAR–IMU SLAM framework that explicitly exploits dual-antenna RTK position and heading measurements. The proposed system first performs LiDAR–IMU fusion within an IESKF-based LIO module to obtain high-rate local state estimates, and then uses these estimates as the prediction in an ESKF, where dual-antenna RTK measurements are incorporated as observations to obtain posterior pose estimates. To better fuse low-rate RTK measurements with high-rate LIO outputs, LiDAR observations are temporally aligned according to RTK timestamps. A measurement quality-check mechanism is further introduced to reduce the adverse impact of degraded RTK signals. Meanwhile, an IMU-based latency compensation module propagates delayed RTK observations to the fusion time before the ESKF update, improving their temporal consistency with the high-rate LIO priors. In addition, we build a GNSS-LiDAR–IMU platform for comprehensive evaluation, motivated by the limited availability of public datasets with dual-antenna RTK. Experimental results demonstrate that introducing the RTK-based correction module improves localization accuracy and global trajectory consistency in large-scale scenarios.