###### Abstract  
To achieve efficient and accurate thick plate welding, as well as to precisely extract and plan the paths of complex three-dimensional weld seams in large steel structures, this study introduces a novel vision-guided approach for robotic welding systems utilizing a constant-focus laser sensor. This methodology specifically targets and mitigates several critical shortcomings inherent in conventional vision-guided welding techniques, including limited detection ranges, diminished precision in both detection and tracking, and suboptimal real-time performance. For preprocessed weld images, an improved grayscale extreme centroid method was developed to extract the center of the light stripe. Furthermore, a sophisticated feature point extraction algorithm, which integrates a maximum distance search strategy with a least-squares fitting procedure, was developed to facilitate the precise and timely identification of weld seam characteristic points. To further optimize the outcomes, a cylindrical filtering mechanism was employed to eliminate substantial discrepancies, whereas local Non-Uniform Rational B-Spline (NURBS) curve interpolation was utilized for the generation of smooth and accurate trajectory plans. A spatial vector-based pose adjustment strategy was then implemented to provide robust guidance for the welding robot, ensuring the successful execution of the welding operations. The experimental results indicated that the proposed algorithm achieved a tracking error of 0.3197 mm for welding workpieces with a thickness of 60 mm, demonstrating the method's substantial potential in the manufacturing sector, especially in the domain of automated welding. S 2024 2024 19 November 2024 20 non-contact operation, high speed, high precision, and stability [4; 5]. However, traditional line laser vision sensors are constrained by the camera's depth of field, allowing them to capture only a limited range of clear images, thus restricting their application in scenarios like thick plate welding. A common approach to enhance the camera's depth of field is to reduce the aperture or shorten the focal length, but this significantly diminishes the resolution of the vision sensor [6]. An alternative approach is to construct a constant-focus optical path within the sensor according to the Scheimpflug principle [7; 8]. This design ensures that all visible detection points on the laser plane are clearly focused, thereby enhancing the depth of field and demonstrating substantial potential for applications in thick plate welding. In recent years, significant attention has been devoted to establishing an inclined camera imaging model that meets the Scheimpflug condition. [PERSON] et al. [9] utilized an ideal non-inclined image plane as a bridge to establish the transformation relationship from the actual inclined image plane to the ideal image plane based on a one-dimensional Scheimpflug tilt angle, thereby deriving an inclined camera imaging model. [PERSON] et al. [10] expanded this transformation concept by extending the Scheimpflug tilt angle to two dimensions and, through theoretical derivation and calibration experiments, demonstrated that describing the inclined camera imaging model with a two-dimensional tilt angle is more comprehensive. [PERSON] et al. [11] proposed a point-line vector model using the physical concept of light propagating in a straight line, establishing an inclined camera imaging model based on the collinearity of intersection points of light rays with both the inclined and ideal image planes. [PERSON] et al. [12] employed the rotational relationship of rigid body transformations to derive a rotation matrix corresponding to the two-dimensional tilt angle, thereby establishing an inclined camera imaging model. [PERSON] et al. [13], based on the classical geometric structure of a thick lens, defined a new camera coordinate system and directly established an inclined camera imaging model that meets the Scheimpflug condition while also proposing corresponding calibration methods.