Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument
碩士 === 逢甲大學 === 航太與系統工程所 === 98 === Geometric primitives-based map is broadly used for mobile robot navigation. This study used line segments as the basic features to build the environment map that focused on data point clustering and line segments extraction. A user-defined threshold value related...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2010
|
Online Access: | http://ndltd.ncl.edu.tw/handle/29427321691820968605 |
id |
ndltd-TW-098FCU05295012 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-098FCU052950122016-04-20T04:18:19Z http://ndltd.ncl.edu.tw/handle/29427321691820968605 Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument 應用LabVIEW虛擬儀控於環境地圖建立之最佳化 Yuan-Kang Huang 黃源港 碩士 逢甲大學 航太與系統工程所 98 Geometric primitives-based map is broadly used for mobile robot navigation. This study used line segments as the basic features to build the environment map that focused on data point clustering and line segments extraction. A user-defined threshold value related to the distance between two consecutive data points is used to clarify different clusters. However, one disadvantage of the existing clustering method is the lack of standardization for the selection of threshold values. Different threshold values are more likely to build different environment map on the same data points. A new approach for data point clustering is proposed to solve aforementioned problems. A laser range finder is used to gather the environment information. Subsequently, the experimental results are conducted through LabVIEW and MATLAB and show fair agreement with the actual environment. Chen-Hung Huang 黃振鴻 2010 學位論文 ; thesis 75 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 逢甲大學 === 航太與系統工程所 === 98 === Geometric primitives-based map is broadly used for mobile robot navigation. This study used line segments as the basic features to build the environment map that focused on data point clustering and line segments extraction. A user-defined threshold value related to the distance between two consecutive data points is used to clarify different clusters. However, one disadvantage of the existing clustering method is the lack of standardization for the selection of threshold values. Different threshold values are more likely to build different environment map on the same data points. A new approach for data point clustering is proposed to solve aforementioned problems. A laser range finder is used to gather the environment information. Subsequently, the experimental results are conducted through LabVIEW and MATLAB and show fair agreement with the actual environment.
|
author2 |
Chen-Hung Huang |
author_facet |
Chen-Hung Huang Yuan-Kang Huang 黃源港 |
author |
Yuan-Kang Huang 黃源港 |
spellingShingle |
Yuan-Kang Huang 黃源港 Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
author_sort |
Yuan-Kang Huang |
title |
Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
title_short |
Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
title_full |
Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
title_fullStr |
Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
title_full_unstemmed |
Optimization of the Environment Map Building by Using the LabVIEW Virtual Instrument |
title_sort |
optimization of the environment map building by using the labview virtual instrument |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/29427321691820968605 |
work_keys_str_mv |
AT yuankanghuang optimizationoftheenvironmentmapbuildingbyusingthelabviewvirtualinstrument AT huángyuángǎng optimizationoftheenvironmentmapbuildingbyusingthelabviewvirtualinstrument AT yuankanghuang yīngyònglabviewxūnǐyíkòngyúhuánjìngdetújiànlìzhīzuìjiāhuà AT huángyuángǎng yīngyònglabviewxūnǐyíkòngyúhuánjìngdetújiànlìzhīzuìjiāhuà |
_version_ |
1718228876312707072 |