Research on Intelligent Pipeline Robot Navigation and Defect Identification Based on Multi-Sensor Fusion

摘要

Faced with the technical bottlenecks of traditional single-sensor methods in dynamic pipeline environments, this study proposes a hierarchical progressive intelligent framework for pipeline robot navigation and defect recognition based on laser-vision-inertial multi-sensor fusion. An innovative Unscented Kalman Filter (UKF)-based nonlinear optimization framework unifies state vectors of navigation and defect recognition to achieve collaborative optimization of pose estimation and feature extraction. The bottom layer realizes multi-source data spatiotemporal alignment via extended Kalman filter; the middle layer completes environmental modeling using improved ICP and visual SLAM（Simultaneous Localization and Mapping）; the top layer adopts lightweight CNN for defect identification. Experiments show that the system achieves centimeter-level positioning accuracy and 92.7% defect recognition accuracy in complex scenes, with cumulative positioning error controlled within ±8 cm and robustness improved by over 40%. It is universal for 200–800 mm pipe diameters, reducing small-diameter navigation drift by 42% and reaching 98.3% detection coverage. Engineering tests verify 4.2× higher inspection efficiency and 65% lower maintenance costs than manual operations. With modular hardware and open software, the system adapts to oil and gas pipelines, utility tunnels and drainage systems, and its cloud-edge knowledge base supports rapid scenario adaptation via transfer learning, providing reusable methodological support for intelligent perception systems.