Distance based neighbor correlation for the segmentation

Ki In Na; Jaemin Byun; Myoungchan Roh; Bumsoo Seo

doi:10.1109/URAI.2013.6677344

Distance based neighbor correlation for the segmentation

Ki In Na, Jaemin Byun, Myoungchan Roh, Bumsoo Seo

Dept. of Mechanical System and Design Engineering

Electronics and Telecommunications Research Institute

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

This paper introduces the segmentation of point cloud with the distance-based connectivity that is originated from the connectivity of local convexity criterion to enhance its accuracy and singularity [1]. The proposed feature is applied to calculate the weighted normal vector and to partition ground and objects respectively through integrating it with other features. The performances of segmentations with the introduced criterion are demonstrated with the labeled simulation data and the real data from 3D LIDAR compared to the original connectivity.

Original language	English
Pages	211-214
Number of pages	4
DOIs	https://doi.org/10.1109/URAI.2013.6677344
State	Published - 2013
Event	2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2013 - Jeju, Korea, Republic of Duration: 30 Oct 2013 → 2 Nov 2013

Conference

Conference	2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2013
Country/Territory	Korea, Republic of
City	Jeju
Period	30/10/13 → 2/11/13

Keywords

3D LIDAR
autonomous vehicle
outdoor navigation
segmentation

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10.1109/URAI.2013.6677344

Cite this

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title = "Distance based neighbor correlation for the segmentation",

abstract = "This paper introduces the segmentation of point cloud with the distance-based connectivity that is originated from the connectivity of local convexity criterion to enhance its accuracy and singularity [1]. The proposed feature is applied to calculate the weighted normal vector and to partition ground and objects respectively through integrating it with other features. The performances of segmentations with the introduced criterion are demonstrated with the labeled simulation data and the real data from 3D LIDAR compared to the original connectivity.",

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T1 - Distance based neighbor correlation for the segmentation

AU - Na, Ki In

AU - Byun, Jaemin

AU - Roh, Myoungchan

AU - Seo, Bumsoo

PY - 2013

Y1 - 2013

N2 - This paper introduces the segmentation of point cloud with the distance-based connectivity that is originated from the connectivity of local convexity criterion to enhance its accuracy and singularity [1]. The proposed feature is applied to calculate the weighted normal vector and to partition ground and objects respectively through integrating it with other features. The performances of segmentations with the introduced criterion are demonstrated with the labeled simulation data and the real data from 3D LIDAR compared to the original connectivity.

AB - This paper introduces the segmentation of point cloud with the distance-based connectivity that is originated from the connectivity of local convexity criterion to enhance its accuracy and singularity [1]. The proposed feature is applied to calculate the weighted normal vector and to partition ground and objects respectively through integrating it with other features. The performances of segmentations with the introduced criterion are demonstrated with the labeled simulation data and the real data from 3D LIDAR compared to the original connectivity.

KW - 3D LIDAR

KW - autonomous vehicle

KW - outdoor navigation

KW - segmentation

UR - https://www.scopus.com/pages/publications/84899115470

U2 - 10.1109/URAI.2013.6677344

DO - 10.1109/URAI.2013.6677344

M3 - Paper

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T2 - 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2013

Y2 - 30 October 2013 through 2 November 2013

ER -

Distance based neighbor correlation for the segmentation

Abstract

Conference

Keywords

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