###### Abstract  
Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrades in vision technology, more and more tasks are being replaced with machine vision. At present, the main method of target recognition used in palletizers is the traditional SURF algorithm, but this method of grasping leads to low accuracy due to the influence of too many mismatched points. Due to the accuracy of robot target localization with binocular-based vision being low, an improved random sampling consistency algorithm for performing complete parallel robot target localization and grasping under the guidance of multi-vision is proposed. Firstly, the improved RANSAC algorithm, based on the SURF algorithm, was created based on the SURF algorithm; next, the parallax gradient method was applied to iterate the matched point pairs several times to further optimize the data; then, the 3D reconstruction was completed using the improved algorithm via the program technique; finally, the obtained data were input into the robot arm, and the camera's internal and external parameters were obtained using the calibration method so that the robot could accurately locate and grasp objects. The experiments show that the improved algorithm shows better recognition accuracy and grasping success with the multi-vision approach. multi-ocular vision; random sampling consistency; 3D reconstruction; parallel robot; target localization and grasping 2023131302023130231  
and depth information for corresponding pixels is inversely proportional. Obtaining an accurate parallax map is crucial in stereo vision [2]. When a person opens and closes their left and right eyes, respectively, they will find that the object appears to be in two different positions. This phenomenon is known as parallax. Similarly, when a binocular camera observes the same object at the same time, the difference between the projected points obtained from the phase planes of the left and right eye cameras is also parallax. Encoding the difference between the horizontal coordinates of the corresponding image points is an important step in obtaining a parallax map. The use of feature-based matching methods to obtain better image information is currently popular according to the literature [3]. In 1999, [PERSON], a professor at Columbia University, first proposed the SIFT algorithm [4], which was used in various fields of vision processing at that time because of its good detection results in occlusion and illumination. In 2006, [PERSON] proposed the SURF algorithm [5], which significantly reduced the inefficiency and improved the robustness of feature mapping by using the Haar wavelet transform [6], Hessian Matrix [7] and integral image [8] approaches. However, due to the possible inaccuracy of the main direction of the SURF algorithm [9], affected by factors such as a large number of similar point features on the edge line [10], it is slightly less effective in matching accuracy, and the problem of mis-matching becomes more and more obvious when the target object has rich texture features. The Random Sample Consensus algorithm, commonly known by its acronym RANSAC [11], was developed by [PERSON] and [PERSON] more than 40 years ago as a novel approach to the robust estimation of the parameters of a model in regression analysis [12]. In order to solve the problem of mismatching, based on the SURF algorithm, in this work, the improved RANSAC algorithm is fused to extract the target image feature points, and the similar points found according to the bidirectional Euclidean distance [13] are judged using the Hessian matrix trace to exclude the feature points that do not meet the requirements. Then, the depth map of the target image is compared with the reconstruction map via the SGBM stereo matching algorithm [14], and the object information is reconstructed in 3D according to the machine-vision-related algorithm. Finally, the robot and the host computer are connected through TCP communication to achieve hand-eye calibration [15] and complete the task.