Point Cloud Library (PCL)
1.7.0
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00001 /** 00002 \addtogroup registration Module registration 00003 00004 \section secRegistrationPresentation Overview 00005 00006 Combining several datasets into a global consistent model is usually performed 00007 using a technique called registration. The key idea is to identify 00008 corresponding points between the data sets and find a transformation that 00009 minimizes the distance (alignment error) between corresponding points. This 00010 process is repeated, since correspondence search is affected by the relative 00011 position and orientation of the data sets. Once the alignment errors fall below 00012 a given threshold, the registration is said to be complete. 00013 00014 The <b>pcl_registration</b> library implements a plethora of point cloud 00015 registration algorithms for both organized an unorganized (general purpose) 00016 datasets. 00017 00018 \image html http://www.pointclouds.org/assets/images/contents/documentation/registration_outdoor.png 00019 \image html http://www.pointclouds.org/assets/images/contents/documentation/registration_closeup.png 00020 00021 \section secRegistrationRequirements Requirements 00022 - \ref common "common" 00023 - \ref kdtree "kdtree" 00024 - \ref sample_consensus "sample_consensus" 00025 - \ref features "features" 00026 00027 */