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Published June 8, 2023 | Version v1
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Energy Efficient RANSAC Algorithm for Flat Surface Detection in Point Clouds

  • 1. National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Description

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene. There are many different approaches for solving object detection problem in point clouds, but most of them require high computational resources. In this work, many variations of the random sample consensus (RANSAC) method are analyzed for objects defined by a mathematical model of an analytical form. Statistical characteristics of data analysis were used to compare the methods. The results demonstrate the most energy efficient flat surface detection method that processes 60 RGB-D camera frames per second.

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Additional details

Identifiers

ISSN
2415-7287

Related works

References
Preprint: 10.48550/arXiv.2011.04862 (DOI)
Journal article: 10.5244/C.23.81 (DOI)
Journal article: 10.1109/TPAMI.2023.3314745 (DOI)

Funding

MASTERLY – Nimble Artificial Intelligence driven robotic solutions for efficient and self-determined handling and assembly operations 101091800
European Commission

Dates

Accepted
2023-06-08

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