High-precision BeiDou/GNSS signals provide users with reliable positioning information. However, achieving high-accuracy, robust autonomous positioning remains a core challenge for mobile robot systems when these signals are obstructed and unavailable. Although significant progress has been made in LiDAR-inertial odometry (LIO) algorithms in recent years, issues such as observation degradation and drift frequently arise in scenarios involving sparse point clouds, field-of-view obstructions, and large-angle rotations, severely impacting system stability and positioning accuracy. Ultra-wideband (UWB) ranging, as a low-power, low-cost absolute positioning technology, offers advantages such as low latency and interference resistance. However, it is highly susceptible to non-line-of-sight (NLOS) errors. Against this backdrop, LiDAR provides high-frequency geometric feature information, IMU maintains short-term continuity, and UWB offers global positional constraints, demonstrating clear complementary strengths among the three. Nevertheless, constructing a targeted fusion framework that leverages sensor strengths to achieve complementary observations and error suppression remains a key challenge in multi-source sensor fusion. To address these issues, this paper designs a tightly coupled LiDAR/UWB/INS model based on the iterated error-state robust Kalman filter (IESRKF), enabling unified modeling of observation residuals and adaptive robust estimation across heterogeneous sensors. By incorporating UWB ranging for absolute position constraints and employing multi-iteration linear optimization, the stability of state convergence is enhanced, effectively mitigating error accumulation and drift in LIO systems during high-angle rotation scenarios. Experimental results demonstrate that the proposed ULIO-IESRKF algorithm maintains superior performance compared to traditional LIO and ULIO-LC algorithms on paths with large rotations and severe obstructions. Furthermore, the multi-iteration linear optimization mechanism effectively mitigates modeling errors introduced by first-order linearization. Compared to the LIO algorithm, the ULIO-IESRKF algorithm achieves improvements of 29.15%, 42.42%, and 30.37% in the E, N, and U directions, respectively. It enhances planar positioning accuracy by 38.00% and improves accuracy in Pitch, Roll, and Yaw by 10.15%, 6.70%, and 34.80%, respectively. Experimental results fully validate that this algorithm achieves high positioning accuracy while demonstrating strong robustness and dynamic adaptability.
