2014 · 33 citations · 17 references
Mathematical ProgrammingEngineeringPoint Cloud ProcessingScan Registration FormulationLocalizationImage AnalysisChannel Capacity EstimationPattern RecognitionImage RegistrationRegistration AccuracyComputational GeometryGeometric ModelingMachine VisionMulti Channel Generalized-icpCurrent StateComputer ScienceMedical Image ComputingSignal ProcessingComputer VisionSpatial VerificationNatural Sciences3D ScanningMulti-view Geometry
Current state of the art scan registration algorithms which use only position information often fall victim to correspondence ambiguity and degeneracy in the optimization solutions. Other methods which use additional channels, such as color or intensity, often use only a small fraction of the available information and ignore the underlying structural information of the added channels. The proposed method incorporates the additional channels directly into the scan registration formulation to provide information within the plane of the surface. This is achieved by calculating the uncertainty both along and perpendicular to the local surface at each point and calculating nearest neighbour correspondences in the higher dimensional space. The proposed method reduces instances of degenerate transformation estimates and improves both registration accuracy and convergence rate. The method is tested on the Ford Vision and Lidar dataset using both color and intensity channels as well as on Microsoft Kinect data obtained from the University of Waterloo campus.
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3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu, Steve Cousins · 2011 · 4.7K citations
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
Marius Muja, David Lowe · 2009 · 2.6K citations · Full text
Object modeling by registration of multiple range images
Y. Chen, Gérard Medioni · 2002 · 1.4K citations