In-motion continuous point cloud measurement based on bundle adjustment fused with motion information of triple line-scan images

Abstract

The point cloud continuous measurements in some in-motion circumstances, such as quality inspection of products on assembly lines or rail traffic, have been requiring higher measurement speed, accuracy, and point cloud density. With the advantages of high acquisition rates and ultrahigh resolution, line-scan cameras have been developing gradually for dynamic measurements. However, because of non-coplanar installation and unidimensional images, the measurement based on line-scan cameras is affected by movement. In this article, a dynamic scanning point cloud measurement based on triple line-scan images is present. The point cloud optimization is based on bundle adjustment fused with motion information. The epipolar constraint of line-scan images in dynamic conditions is researched for matching. The effect of motion on matching error is analyzed. A triple line-scan cameras experimental setup validates the proposed method.