Mobile Robot Indoor SLAM by Using Sensor Fusion
碩士 === 大同大學 === 機械工程學系(所) === 97 === An autonomous mobile robot must possess capabilities of cognition, motion planning and navigation. Consequently, a robot has to localize its position and build map simultaneously in unknown environments. In this thesis, we study SLAM (simultaneously localization...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2009
|
Online Access: | http://ndltd.ncl.edu.tw/handle/94563313215350803123 |
id |
ndltd-TW-097TTU05311037 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-097TTU053110372016-05-02T04:11:11Z http://ndltd.ncl.edu.tw/handle/94563313215350803123 Mobile Robot Indoor SLAM by Using Sensor Fusion 感測器融合運用於移動式機器人之室內同步定位與建地圖 Chia-Chien Chang 張嘉鑑 碩士 大同大學 機械工程學系(所) 97 An autonomous mobile robot must possess capabilities of cognition, motion planning and navigation. Consequently, a robot has to localize its position and build map simultaneously in unknown environments. In this thesis, we study SLAM (simultaneously localization and mapping) of mobile robot employing monocular vision and odometer. Moreover, experiments were applied to evaluate the performance of the proposed mechanism. First the vision picture was processed to derive the objects’ vertical edge landmark. The relative coordinates were than defined and reduced from three-dimension to two-dimension to simply computation. Consequently, the robot’s trajectories derived using the data of odometer were applied to build the robot’s dynamic model. In this study, the extended Kalman filter was employed to eliminate the accumulated error from odometer measurement and noise. The states of mobile robot and landmarks were estimated and updated continuously. The localization of mobile robot was achieved through the matching of landmarks. Moreover, laser range finder was utilized to draw the map of unknown environment. Guan-Chun Luh 陸冠群 2009 學位論文 ; thesis 45 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 大同大學 === 機械工程學系(所) === 97 === An autonomous mobile robot must possess capabilities of cognition, motion planning and navigation. Consequently, a robot has to localize its position and build map simultaneously in unknown environments. In this thesis, we study SLAM (simultaneously localization and mapping) of mobile robot employing monocular vision and odometer. Moreover, experiments were applied to evaluate the performance of the proposed mechanism.
First the vision picture was processed to derive the objects’ vertical edge landmark. The relative coordinates were than defined and reduced from three-dimension to two-dimension to simply computation. Consequently, the robot’s trajectories derived using the data of odometer were applied to build the robot’s dynamic model. In this study, the extended Kalman filter was employed to eliminate the accumulated error from odometer measurement and noise. The states of mobile robot and landmarks were estimated and updated continuously. The localization of mobile robot was achieved through the matching of landmarks. Moreover, laser range finder was utilized to draw the map of unknown environment.
|
author2 |
Guan-Chun Luh |
author_facet |
Guan-Chun Luh Chia-Chien Chang 張嘉鑑 |
author |
Chia-Chien Chang 張嘉鑑 |
spellingShingle |
Chia-Chien Chang 張嘉鑑 Mobile Robot Indoor SLAM by Using Sensor Fusion |
author_sort |
Chia-Chien Chang |
title |
Mobile Robot Indoor SLAM by Using Sensor Fusion |
title_short |
Mobile Robot Indoor SLAM by Using Sensor Fusion |
title_full |
Mobile Robot Indoor SLAM by Using Sensor Fusion |
title_fullStr |
Mobile Robot Indoor SLAM by Using Sensor Fusion |
title_full_unstemmed |
Mobile Robot Indoor SLAM by Using Sensor Fusion |
title_sort |
mobile robot indoor slam by using sensor fusion |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/94563313215350803123 |
work_keys_str_mv |
AT chiachienchang mobilerobotindoorslambyusingsensorfusion AT zhāngjiājiàn mobilerobotindoorslambyusingsensorfusion AT chiachienchang gǎncèqìrónghéyùnyòngyúyídòngshìjīqìrénzhīshìnèitóngbùdìngwèiyǔjiàndetú AT zhāngjiājiàn gǎncèqìrónghéyùnyòngyúyídòngshìjīqìrénzhīshìnèitóngbùdìngwèiyǔjiàndetú |
_version_ |
1718253418031611904 |