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

Abstract

With the increasing complexity of pipeline inspection and maintenance requirements, traditional single sensor methods have exposed significant technical bottlenecks in dealing with changing pipeline environments. This study proposes an intelligent pipeline robot navigation and defect recognition framework based on multi-sensor fusion. By integrating the complementary characteristics of lidar, visual sensors, and inertial measurement units (IMUs), a laser vision inertial multimodal data fusion system is constructed. The system adopts a hierarchical progressive architecture, with the bottom layer achieving spatiotemporal alignment and dynamic compensation of multi-source data through extended Kalman filtering. The middle layer uses improved ICP algorithm and visual SLAM technology to complete environmental modeling and pose estimation. The top layer is based on lightweight convolutional neural networks to achieve accurate recognition of defect types and positions. The experiment shows that the proposed method achieves centimeter level positioning accuracy and 92.7% defect recognition accuracy in complex pipeline scenes, significantly improving the system's environmental adaptability and detection reliability under interference such as lighting changes and metal reflections compared to traditional methods.

At the level of multimodal data fusion, an innovative nonlinear optimization framework based on UKF has been proposed, which achieves collaborative optimization of pose estimation and feature extraction by unifying the state vectors of modeling navigation and defect recognition tasks. Experimental verification shows that the framework can control the cumulative positioning error within ± 8cm in typical complex scenarios such as pipeline bends and branch nodes, and improve system robustness by more than 40% through multi-sensor redundancy design. In response to the detection requirements of different pipe diameters, the system successfully verified its universality within the range of 200mm to 800mm pipe diameters by adaptively adjusting sensor scanning parameters and robotic arm configurations. The navigation drift in small pipe diameter scenarios was reduced by 42% compared to traditional methods, and the coverage rate of large-scale pipeline detection reached 98.3%. Engineering application testing has shown that this technology improves pipeline inspection efficiency by 4.2 times that of manual operation, reduces operation and maintenance costs by about 65%, and the related achievements have been industrialized in the transformation project of material feeding robots in petrochemical enterprises, significantly reducing equipment downtime and manual intervention frequency.

The study further explored the cross domain migration potential of multi-sensor fusion technology, and quickly adapted the core algorithm to scenarios such as oil and gas pipelines, urban comprehensive pipe galleries, and industrial drainage systems through modular hardware design and open software architecture. In oil and gas pipeline inspection, the system has achieved a defect recognition accuracy of 0.1mm level; In the application of comprehensive pipe gallery, the positioning accuracy reaches ± 3cm; in industrial drainage scenarios, the accuracy of low light environment recognition remains at 89.7%. The defect knowledge base based on cloud edge collaboration has accumulated over 100000 sets of sample data, supporting rapid adaptation to new scene requirements through transfer learning. This study not only provides high-precision and strong robustness technical solutions for the field of pipeline inspection, but also proposes a layered fusion architecture and adaptive optimization strategy, which provides reusable methodological support for a wider range of engineering scenarios such as mobile robots and industrial monitoring, promoting the evolution of intelligent perception systems towards global intelligent monitoring networks.