Outlier-Aware LiDAR–IMU–UWB Integration With NLOS Mitigation and Anchor Self-Calibration in Complex Scenarios
Abstract
LiDAR–inertial odometry (LIO) is susceptible to long-term drift in global navigation satellite system (GNSS)-challenged environments, limiting accurate state estimation for unmanned aerial vehicles (UAVs), mobile robots, and autonomous ground vehicles. Ultrawideband (UWB) infrastructure can provide ranging constraints to mitigate this drift. However, its practical utility is limited by two critical bottlenecks: the labor-intensive requirement for precise anchor precalibration and measurement degradation caused by nonline-of-sight (NLOS) propagation. To address these challenges, we propose a UWB-aided continuous-time fusion framework that tightly couples LiDAR, inertial measurement unit (IMU), and UWB data using nonuniform B-splines on Lie groups within a factor graph optimization (FGO) structure. First, to reduce the reliance on precise anchor positions, we formulate UWB anchor positions as static anchor states and jointly optimize them with the continuous-time vehicle trajectory under rough prior initialization. This design enables asynchronous range measurements to jointly constrain the spline trajectory and anchor position. Second, a map-aware NLOS mitigation strategy based on geometric penetration depth (GPD) is developed. GPD estimates physical obstructions along UWB propagation paths from the LiDAR-generated map and converts them into continuous measurement uncertainty, thereby avoiding residual-based robust kernels that suppress measurements only after large residuals emerge. The proposed method is evaluated on a public vehicle-infrastructure urban dataset and multiple self-collected datasets covering complex urban and controlled indoor environments. Experimental results demonstrate that the proposed method outperforms LiDAR–inertial, UWB–inertial, and discrete-time UWB–LiDAR–inertial baselines in both localization accuracy and robustness while achieving long-term drift-bounded localization in GNSS-challenged environments. To facilitate further research, our implementation and datasets are available at https://github.com/PolyU-TASLAB/LIUO A video demonstration is available at https://youtu.be/pGqk48h-OBc