Geometric constraints from planar surface pattern matching
Image and Vision Computing
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Rigid body motion from range image sequences
CVGIP: Image Understanding
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Journal of Intelligent and Robotic Systems
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Automatic Registration of Range Images Based on Correspondence of Complete Plane Patches
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Fully Automatic Registration of 3D Point Clouds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles
Journal of Field Robotics - Special Issue on Mining Robotics
Journal of Field Robotics - Three-Dimensional Mapping, Part 3
Evaluation of 3D registration reliability and speed: a comparison of ICP and NDT
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
3-D Motion Estimation Using Range Data
IEEE Transactions on Intelligent Transportation Systems
Spectral registration of noisy sonar data for underwater 3D mapping
Autonomous Robots
Visual SLAM Based on Rigid-Body 3D Landmarks
Journal of Intelligent and Robotic Systems
Challenging data sets for point cloud registration algorithms
International Journal of Robotics Research
Exploring high-level plane primitives for indoor 3d reconstruction with a hand-held RGB-D camera
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
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We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms, such as the 3-D normal distribution transform (NDT). The simpler case of known plane correspondences is tackled first by deriving expressions for leastsquares pose estimation considering plane-parameter uncertainty computed during plane extraction. Closed-form expressions for covariances are also derived. To round-off the solution, we present a new algorithm, which is called minimally uncertain maximal consensus (MUMC), to determine the unknown plane correspondences by maximizing geometric consistency by minimizing the uncertainty volume in configuration space. Experimental results from three 3-D sensors, viz., Swiss-Ranger, University of South Florida Odetics Laser Detection and Ranging, and an actuated SICK S300, are given. The first two have low fields of view (FOV) and moderate ranges, while the third has a much bigger FOV and range. Experimental results show that this approach is not only more robust than point- or grid-based approaches in plane-rich environments, but it is also faster, requires significantly less memory, and offers a less-cluttered planar-patches-based visualization.