features. The proposed on-line method starts with visual odometry to estimate the ego-motion and to register point clouds from a scanning lidar at a high frequency but low fidelity. Then, scan matching based lidar odometry refines the motion estimation and point cloud registration simultaneously. We show

. A more efficient approach uses a 2D Hough transform to estimate orientation followed by a 3D Hough transform to detect position, but still has extensive runtimes and lacks robustness in dense point cloud data. This work endeavors to (1) further decrease the runtime for cylinder feature extraction

of the generalized ICP algorithm based on a plane-to-plane metric. Substantially, we exploit the structure of the point clouds, in particular we take advantage of the information given by the surface normals of each point. A closed form solution is developed for the least-squares estimation prob-lem used to register

this method within the scope of PCL (Point Cloud Library) as a branch of the code for large scale KinectFusion, where the original ICP system for odometry estimation has been completely substituted by our method. A PCL fork including the modified method is available for download. 1

modeling applications. In this paper, we propose a new approach which cans register point clouds automatically and robustly without pose information. This method makes an extension of SLAM6D in two aspects: 1) The modified surf descriptor is used to find corresponding point pairs to estimate the initial

We present a new volumetric method for reconstructing watertight triangle meshes from arbitrary, unoriented point clouds. While previous techniques usually reconstruct surfaces as the zero level-set of a signed distance function, our method uses an unsigned distance function and hence does

about the environment. To this end, we employ a continuously updated point cloud to estimate the camera poses based on 2d-3d-correspondences. Within this set of camera poses, we identify keyframes which are combined into a sliding window and refined by bundle adjustment. Subsequently, we update

Abstract — This paper reports a novel mutual information (MI) based algorithm for automatic registration of unstructured 3D point clouds comprised of co-registered 3D lidar and camera imagery. The proposed method provides a robust and principled framework for fusing the complementary information

Abstract — We present a robust plane finding algorithm that when combined with plane-based frame-to-frame registration gives accurate real-time pose estimation. Our plane extraction is capable of handling large and sparse datasets such as those generated from spinning multi-laser sensors

vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusionvolume due tothisvariation, and,(iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi