# Feature Point Identification in Filtel Weld Joints Using an Improved CPDA Method
## 1 Introduction  
As a key method to achieve a permanent connection between materials, welding is becoming more and more important in manufacturing [1, 2], bridge and/or ship construction [3, 4], vehicle engineering [5], the aerospace industry [6], and so on. Moreover, with the rapid development of the industrial robot [7], the application of welding processes will continue to be broadened with automation and intelligence. To meet the advancements of industrial modernization and the shortage of labor resources, the use of robot-aided automated welding systems has become a surging need in the welding fields [8]. Welding processes with robots are mainly achieved by three means: the teach and playback mode, offline programming, and vision-based automation. Due to the complexity of the working environment in welding processes with varied welding joint types, welding robots using the teach and playback mode are limited in meeting the requirements of flexible manufacturing [9]. Meanwhile, the offline programming mode is time- and labor-inefficient, especially in small-batch, customized production [10]. With the development of machine-vision sensing technology [11], automated welding robots with visual perception are becoming a promising solution to meet the developing trend of flexible and intelligent manufacturing. In the sensing system of an automated welding robot, the recognition and positioning method of the weld seam is crucial. The accuracy of the methods for determining the weld seam position will greatly affect the automation robustness and the quality of the welding joints [12]. The visual sensors used in welding robots can be mainly divided into two types: passive sensing and active sensing. Commonly, studies on passive sensing methods for extracting the 3D position of a weld seam using stereo vision have focused on image processing algorithms. An automatic weld seam recognition method was developed using a stereo matching algorithm to obtain the 3D points of a square weld seam [13]. This method was further extended to the recognition of fillet weld joints using a developed adaptive line-growing algorithm based on the difference in gray-scale values in each pixel near the weld seam region [14]. To improve the detection accuracy, not only the feature points or lines but also the feature descriptor (e.g., binary robust independent elementary features, BRIEF) were used in stereo matching to obtain the 3D information of the fillet weld seam [15]. Both the 3D shape of the seam and the structure of the fillet weld can be measured for controlling the trajectory and posture of a robot torch. However, the passive visual sensing method is sensitive to the changing of light, while strong lightening occurs in the welding process. The passive vision method has poor robustness in the complex welding environment and is invalid during the welding process. By utilizing the laser and/or structured light as the auxiliary light source, active visual sensing methods are found to be more robust in a complex industrial environment [16]. Active vision with laser light has the advantages of non-contact, high precision, and computational efficiency and has been widely used in welding guidance [17] and 3D detection [18]. The key challenge in the automatic guided welding robot based on an active laser sensing system is to identify the geometrical features of the welding seam in the captured image of a laser strip. The performance of feature point identification methods is directly related to the precision, accuracy, and speed of welding guidance. At present, the commonly used laser stripe feature extraction methods in robotic welding guidance include least-square and/or curve fitting [19], template matching and/or polygon approximation [20], sliding vector and Hough transform analysis [21], and corner-detection-based methods [22]. Active visual sensing methods with laser light have been studied and implemented in robotic welding applications by researchers [23; 24; 25].