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Depth-image segmentation based on evolving principles for 3D sensing of structured indoor environments
ID Antić, Miloš (Author), ID Zdešar, Andrej (Author), ID Škrjanc, Igor (Author)

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Abstract
This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.

Language:English
Keywords:depth sensor, line extraction, flat surface extraction, evolving clustering, machine vision, smart sensors
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FE - Faculty of Electrical Engineering
Publication status:Published
Publication version:Version of Record
Year:2021
Number of pages:30 str.
Numbering:Vol. 21, iss. 13, art. 4395
PID:20.500.12556/RUL-135600 This link opens in a new window
UDC:681.5:004
ISSN on article:1424-8220
DOI:10.3390/s21134395 This link opens in a new window
COBISS.SI-ID:74209795 This link opens in a new window
Publication date in RUL:22.03.2022
Views:727
Downloads:132
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Record is a part of a journal

Title:Sensors
Shortened title:Sensors
Publisher:MDPI
ISSN:1424-8220
COBISS.SI-ID:10176278 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:01.07.2021

Secondary language

Language:Slovenian
Keywords:globinski senzorji, ekstrakcija daljic, ekstrakcija ravnih površin, samorazvijajoče se rojenje, strojni vid, pametni senzorji

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P2-0219
Name:Modeliranje, simulacija in vodenje procesov

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