Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. The PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors, as well as methods for refining initial alignments using different variants of the well-known iterative closest point (ICP) algorithm. This article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3-D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in the PCL. These examples include dense red-greenblue-depth (RGB-D) point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3-D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3-D scanner on a microaerial vehicle (MAV). Registration of 3-D Point CloudsThe problem of consistently aligning two or more point clouds, i.e., sets of 3-D points, is inherent for 3-D registration. Often the point clouds are acquired by 3-D sensors from different viewpoints. The registration finds the relative pose (position and orientation) between views in a global coordinate frame, such that the overlapping areas between the point clouds match as well as possible; for two examples of registration see Figure 1. The overall objective of registration is to align individual point clouds and fuse them to a single point cloud so that subsequent

Abstract. Real-time 3D perception of the surrounding environment is a crucial precondition for the reliable and safe application of mobile service robots in domestic environments. Using a RGB-D camera, we present a system for acquiring and processing 3D (semantic) information at frame rates of up to 30Hz that allows a mobile robot to reliably detect obstacles and segment graspable objects and supporting surfaces as well as the overall scene geometry. Using integral images, we compute local surface normals. The points are then clustered, segmented, and classified in both normal space and spherical coordinates. The system is tested in different setups in a real household environment. The results show that the system is capable of reliably detecting obstacles at high frame rates, even in case of obstacles that move fast or do not considerably stick out of the ground. The segmentation of all planes in the 3D data even allows for correcting characteristic measurement errors and for reconstructing the original scene geometry in far ranges.

Abstract-Time-of-Flight cameras constitute a smart and fast technology for 3D perception but lack in measurement precision and robustness. The authors present a comprehensive approach for 3D environment mapping based on this technology. Imprecision of depth measurements are properly handled by calibration and application of several filters. Robust registration is performed by a novel extension to the Iterative Closest Point algorithm. Remaining registration errors are reduced by global relaxation after loop-closure and surface smoothing. A laboratory ground truth evaluation is provided as well as 3D mapping experiments in a larger indoor environment.

et al.: 3D Mapping with Time-of-Flight Cameras • 935(SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for threedimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. The paper first summarizes calibration and filtering approaches for improving the accuracy, precision, and robustness of ToF cameras independent of their intended usage. Then, several ego motion estimation approaches are applied or adapted, respectively, in order to provide a performance benchmark for registering ToF camera data. As a part of this, an extension to the iterative closest point algorithm has been developed that increases the robustness under restricted field of view and under larger displacements. Using an indoor environment, the paper provides results from SLAM experiments using these approaches in comparison. It turns out that the application of ToF cameras is feasible to SLAM tasks, although this type of sensor has a complex error characteristic. C 2009 Wiley Periodicals, Inc.

Abstract-Decomposing sensory measurements into relevant parts is a fundamental prerequisite for solving complex tasks, e.g., in the field of mobile manipulation in domestic environments. In this paper, we present a fast approach to surface reconstruction in range images by means of approximate polygonal meshing. The obtained local surface information and neighborhoods are then used to 1) smooth the underlying measurements, and 2) segment the image into planar regions and other geometric primitives. An evaluation using publicly available data sets shows that our approach does not rank behind state-of-the-art algorithms while allowing to process range images at high frame rates.