Range data fusion for accurate surface generation from heterogeneous range scanners

In this paper, we present a new method for range data fusion from two heterogeneous range scanners for accurate surface modeling of rough and highly unstructured terrain. First, we present the segmentation of RGB-D images using the new framework of the GMM by employing the convex relaxation technique. After segmentation of RGB-D images, we transform both the range data to a common reference frame using PCA algorithm and apply the ICP algorithm to align both data in the reference frame. Based on a threshold criterion, we fuse the range data in such a way that the coarser regions are obtained from Kinect sensor and finer regions of plane are obtained from the Laser range sensor. After fusion, we apply Delaunay triangulation algorithm to generate the highly accurate surface model of the terrain. Finally, the experimental results show the robustness of the proposed approach.