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Probabilistic Primitive Refinement algorithm for colored point cloud data
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. (CVAP)
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.ORCID-id: 0000-0003-0448-3786
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS. (CAS/CVAP/CSC)ORCID-id: 0000-0002-7796-1438
KTH, Skolan för datavetenskap och kommunikation (CSC).ORCID-id: 0000-0002-1170-7162
2015 (engelsk)Inngår i: 2015 European Conference on Mobile Robots (ECMR), Lincoln: IEEE conference proceedings, 2015Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this work we present the Probabilistic Primitive Refinement (PPR) algorithm, an iterative method for accurately determining the inliers of an estimated primitive (such as planes and spheres) parametrization in an unorganized, noisy point cloud. The measurement noise of the points belonging to the proposed primitive surface are modelled using a Gaussian distribution and the measurements of extraneous points to the proposed surface are modelled as a histogram. Given these models, the probability that a measurement originated from the proposed surface model can be computed. Our novel technique to model the noisy surface from the measurement data does not require a priori given parameters for the sensor noise model. The absence of sensitive parameters selection is a strength of our method. Using the geometric information obtained from such an estimate the algorithm then builds a color-based model for the surface, further boosting the accuracy of the segmentation. If used iteratively the PPR algorithm can be seen as a variation of the popular mean-shift algorithm with an adaptive stochastic kernel function.

sted, utgiver, år, opplag, sider
Lincoln: IEEE conference proceedings, 2015.
Emneord [en]
Registration, poiint cloud, robotics
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
URN: urn:nbn:se:kth:diva-178942DOI: 10.1109/ECMR.2015.7324199ISI: 000380213600033Scopus ID: 2-s2.0-84962333311OAI: oai:DiVA.org:kth-178942DiVA, id: diva2:878492
Konferanse
2015 European Conference on Mobile Robots (ECMR)
Prosjekter
STRANDS
Forskningsfinansiär
EU, FP7, Seventh Framework Programme, 600623
Merknad

QC 20160114

Tilgjengelig fra: 2015-12-09 Laget: 2015-12-09 Sist oppdatert: 2016-09-05bibliografisk kontrollert

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Folkesson, JohnJensfelt, Patric

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Ekekrantz, JohanThippur, AkshayaFolkesson, JohnJensfelt, Patric
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