Objective: Long-distance oil and gas transmission pipelines are important energy infrastructures. Currently, there are deficiencies in the automatic tracking accuracy and adaptability of external welding machines during pipeline construction. Operators often need to manually adjust external welding equipment (welding torch) to ensure the quality of the joints. Improving the intelligence of the welding process is an effective way to improve the efficiency and joint qualification rate during the on-site laying of long oil and gas pipelines. This study proposes a detection and control algorithm for the welding torch position and posture, applicable to all position welding of workpieces with arbitrary spatial postures. Methods: This study is the first to design a multisource sensor that combines laser-structured light vision sensing with dual-axis tilt sensing. This multisource sensor combines the advantages of both types of sensing, enabling it to detect the relative position information of the welding torch, as well as the posture information of the welding torch and workpiece. Using this multisource sensor, the algorithm performs integrated calculations of the welding groove size parameters and relative position and posture parameters of the welding torch under any workpiece posture through local groove surface reconstruction. This method fully uses laser line data from images to ensure stable, applicable, and accurate parameter calculations. Through coordinate transformation, the spatial posture (α_w and β_w) of the local workpiece can be obtained. These integrated feature parameters provide the basis for controlling the welding torch's spatial position and posture in any pipeline space all-position welding. Next, a pipeline intelligent welding system with five degrees of freedom based on multisource sensing is constructed. The system, combined with the designed algorithm, achieves real-time control of the welding torch position and posture (e, H, α, and β), meeting welding process requirements and enabling high-quality weld formation control during arc welding. Results: The experimental results show that the attitude angle feedback control error of the welding torch did not exceed 0.8°, the lateral position tracking deviation was within 0.25 mm, and the height tracking deviation did not exceed 0.63 mm during the pipeline all-position welding process. Compared to existing welding seam detection and tracking systems based on structured light-vision sensing, the proposed algorithm offers superior accuracy and stability. It detects not only the position deviation of the welding torch but also the posture of the welding joint on any unstructured surface with an unknown spatial posture. Conclusions: The proposed algorithm for detecting and controlling the position and posture of the welding torch can be used to achieve accurate control during pipeline space all-position welding. This advancement significantly improves the intelligence level of pipeline external welding equipment and provides technical support for controlling the position and posture control of the welding torch when welding unknown posture-curved workpieces.