A Point Cloud Alignment Algorithm Based on Stereo Vision Using Random Pattern Projection

Abstract

This paper proposes a point cloud alignment algorithm based on stereo vision using Random Pattern Projection (RPP). In the application of stereo vision, it is rather difficult to find correspondences between stereo images of texture-less objects. To overcome this issue, RPP is used to enhance the object’s features, thus increasing the accuracy of the identified correspondences of the stereo images. In the 3D alignment algorithm, the down sample technique is used to filter out the outliers of the point cloud data to improve system efficiency. Furthermore, the extracted features of the down sample point cloud data were applied in the matching process. Finally, the object’s pose was estimated by the alignment algorithm based on object features. In experiments, the maximum error and standard deviation of rotation are respectively about 0.031°and 0.199°, while the maximum error and standard deviation of translation are respectively about 0.565 mm and 0.902 mm . The execution time for pose estimation is about 230ms.