Robust sensor fusion and localization correction for tunnel inspection robots via a probabilistic baseline model

Autonomous tunnel inspection robots are essential for intelligent infrastructure management and the construction of structural digital twins. However, in GPS-denied and slippery underground environments, non-systematic errors such as wheel slip frequently corrupt odometry data, severely degrading the spatial fidelity of structural defect mapping. To bridge the gap between robotic navigation and reliable infrastructure assessment, this paper proposes an unsupervised, data-driven anomaly detection and state correction framework based on a probabilistic baseline model. Initially, by analyzing the coupled motion response of the onboard sensor fusion system, a probabilistic baseline model is established via Kernel Density Estimation (KDE) to learn the nominal kinematic data distribution. A diagnostic factor is then derived to detect abnormal slip conditions in real-time. Subsequently, robust sensor fusion is achieved by integrating this probabilistic model into the observation equations of an Extended Kalman Filter (EKF), effectively rectifying the robot's motion state. The proposed framework is validated through numerical simulations and physical experiments in an operational subway tunnel. Results demonstrate that the proposed approach outperforms four mainstream unsupervised anomaly detection methods (autoencoder, variational autoencoder, isolation forest, and one-class SVM) in diagnosis accuracy, inference latency, and memory footprint, while maintaining exceptional robustness against severe noise interference. Moreover, field validation on an operational subway tunnel with real induced wheel slip confirms a slip diagnosis accuracy of 99.12%, with the longitudinal positioning error converging to within 0.2 m after correction.