Depth-image segmentation based on evolving principles for 3D sensing of structured indoor environments

Antić, Miloš (author), Zdešar, Andrej (author), Škrjanc, Igor (author)

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
• UDC: 681.5:004
• ISSN on article: 1424-8220
• DOI: 10.3390/s21134395
• COBISS.SI-ID: 74209795
• Publication date in RUL: 22.03.2022

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

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