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Robust Object Segmentation Using a Multi-Layer Laser Scanner
The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279490/ https://www.ncbi.nlm.nih.gov/pubmed/25356645 http://dx.doi.org/10.3390/s141120400 |
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author | Kim, Beomseong Choi, Baehoon Yoo, Minkyun Kim, Hyunju Kim, Euntai |
author_facet | Kim, Beomseong Choi, Baehoon Yoo, Minkyun Kim, Hyunju Kim, Euntai |
author_sort | Kim, Beomseong |
collection | PubMed |
description | The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations. |
format | Online Article Text |
id | pubmed-4279490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-42794902015-01-15 Robust Object Segmentation Using a Multi-Layer Laser Scanner Kim, Beomseong Choi, Baehoon Yoo, Minkyun Kim, Hyunju Kim, Euntai Sensors (Basel) Article The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations. MDPI 2014-10-29 /pmc/articles/PMC4279490/ /pubmed/25356645 http://dx.doi.org/10.3390/s141120400 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Beomseong Choi, Baehoon Yoo, Minkyun Kim, Hyunju Kim, Euntai Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title | Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title_full | Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title_fullStr | Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title_full_unstemmed | Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title_short | Robust Object Segmentation Using a Multi-Layer Laser Scanner |
title_sort | robust object segmentation using a multi-layer laser scanner |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279490/ https://www.ncbi.nlm.nih.gov/pubmed/25356645 http://dx.doi.org/10.3390/s141120400 |
work_keys_str_mv | AT kimbeomseong robustobjectsegmentationusingamultilayerlaserscanner AT choibaehoon robustobjectsegmentationusingamultilayerlaserscanner AT yoominkyun robustobjectsegmentationusingamultilayerlaserscanner AT kimhyunju robustobjectsegmentationusingamultilayerlaserscanner AT kimeuntai robustobjectsegmentationusingamultilayerlaserscanner |