RC-LIO: 退化环境中多传感器融合补偿的激光雷达惯性里程计

LiDAR-based simultaneous localization and mapping has been widely applied in mobile robotics and autonomous driving. However, in degraded environments such as rain, snow, and dust, laser beams are easily disturbed by particle scattering, generating a large number of noise points, which leads to map distortion and localization drift. Thus, a millimeter-wave radar-compensated intensity-based dynamic statistical outlier removal method (RC-IDSOR) is proposed to filter laser noise points in real time while preserving environmental structural features. Furthermore, a radar-compensated LiDAR-inertial odometry (RC-LIO) is developed. First, a dynamic local covariance is optimized and an intensity confidence weighting mechanism is designed to improve the stability of generalized ICP matching. Then, a second-order compensation term is incorporated into the error-state Kalman filter prediction to enhance IMU propagation accuracy under highly dynamic scenes. Experimental results show that RC-IDSOR achieves an average F-score exceeding 0.85 on the WADS dataset, with a precision improvement of about 6.8%. RC-LIO attains an average absolute trajectory error of about 0.33 m in degraded SubT-MRS scenarios, and reduces localization error by about 49.6% in heavy-rain environments on the Snail-Radar dataset compared with non-filtering odometry. Finally, RC-LIO is deployed on industrial vehicles operating in heavy-dust environments, where experiments demonstrate short-term repeatability errors below 5.6 cm and stable long-term operation, validating its real-time performance and engineering feasibility.