Tightly Coupled LiDAR-IMU Positioning System Based on Semiconductor Optoelectronic LiDAR Sensing with Prior Map Constraints and Robust Relocalization Mechanism

Accurate and robust localization based on semiconductor optoelectronic LiDAR sensing is a fundamental prerequisite for autonomous navigation of mobile robots in complex scenarios. Traditional positioning methods based on semiconductor optoelectronic LiDAR sensors suffer from cumulative drift during long-term operation, while prior map-based positioning techniques often lack adaptability to dynamic environments. To address these challenges, this paper proposes a high-performance LiDAR-IMU tightly coupled positioning system integrating prior map constraints, LIO optimization, and an adaptive failure detection-relocalization mechanism. A high-precision global map constructed by LIO-SAM serves as the prior constraint to ensure global consistency of pose estimation, while the tightly coupled LIO framework maintains high accuracy and low latency in high-dynamic scenarios. The proposed dual-index failure detection strategy identifies localization anomalies in real time, and a Bag-of-Words (BoW)-based relocalization module rapidly restores precise positioning. Extensive simulations on MARSIM and physical experiments in structured, semi-structured, and feature-sparse environments demonstrate that the proposed system outperforms state-of-the-art (SOTA) methods including LIO-SAM, Ada-LIO, and Map-ICP. Specifically, the system achieves an Absolute Trajectory Error (ATE) root mean square (RMSE) of ≤0.06 m in physical experiments, a cumulative drift of ≤0.1 m per 100 m, and a relocalization success rate of ≥90% in feature-sparse scenes. These results validate the system’s superiority in accuracy, robustness, and real-time performance, providing a reliable signal-processing solution for semiconductor optoelectronic LiDAR-based sensing systems in complex practical applications.