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
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SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
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IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
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Proceedings of the 24th annual conference on Computer graphics and interactive techniques
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STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Visualization and Computer Graphics
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
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3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Journal of Field Robotics - Three-Dimensional Mapping, Part 3
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ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Fast 3D mapping by matching planes extracted from range sensor point-clouds
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
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This paper introduces a methodology to cluster noisy range images into planar regions acquired in indoor environments. The noisy range images are segmented based on a Gaussian similarity metric, which compares the geometric attributes that satisfy the coplanarity conditions. The algorithm is designed to cluster coplanar noisy range data by means of patch-based sampling from range images. We discuss the advantages of patch-based clustering over point-based clustering of noisy range images that eliminates computational redundancy to accelerate the clustering process while keeping the segmentation error to a minimum. The final output of the algorithm is a set of polygons, where each polygon is defined by a set of boundary points that replaces large number of coplanar data points in a given planar region. The 3D range image is acquired by a rotating 2D range scanner and stored in a 2D array. Each element in the array is explicitly stored as the range distance; the indices of the array implicitly retain neighborhood and angular information. The array is grouped into mutually-exclusive patches of size (kxk) and the Hessian plane parameters are computed for each patch. We propose a graph-search algorithm that compares the plane parameters of neighboring patches by searching breadth-wise and clusters the coplanar patches into respective planes. We compare the proposed Patch-based Plane Clustering (PPC) algorithm with the point-based Region Growing (RG) algorithm and the RANSAC plane segmentation method to analyze the performance of each of the algorithms in terms of speed and accuracy. Experimental results indicate that the PPC algorithm shows a significant improvement in computational speed when compared with the state-of-the-art segmentation algorithms while maintaining a high accuracy in segmenting noisy range images.