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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80934完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李綱(Kang Li) | |
| dc.contributor.author | Chun-Ju Chen | en |
| dc.contributor.author | 陳俊儒 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:22:35Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:22:35Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80934 | - |
| dc.description.abstract | 為解決機器人定位系統之初始姿態估測與機器人綁架問題，本研究之深度學習模型藉由單張RGB影像預測位於地圖中的六維姿態，並且可實時運作於嵌入式系統上。在特殊結構的卷積神經網絡基礎下，加入幾何限制模組以及輔助學習方法增加模型的準確性，以此深度學習模型預測初始姿態。基於此深度學習模型，本研究開發一套定位失效偵測演算法，藉由評估目前主要定位系統長期與短期的代價變化，做到實時運算、低誤判率，且高失效偵測率的結果。最後經由實驗可證明，本研究模型運算速度可達到現今地圖座標迴歸模型(map coordinate regression model)最快的4.7ms，並且在嵌入式系統上達到19.15fps的速度。於室外資料集達到平均誤差1.51m與7.79°、室內資料集平均誤差0.40m與1.78°的精度；此外，定位失效偵測演算法實驗當中達到0.98~1.00的精準率與0.78~1.00的召回率，顯示演算法的低誤判率與高定位失效偵測成功率。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:22:35Z (GMT). No. of bitstreams: 1 U0001-1409202110324400.pdf: 4009491 bytes, checksum: 993f328351d6e8cc827e0d978f35847a (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書...# 誌謝...i 中文摘要...ii ABSTRACT...iii CONTENTS...iv LIST OF FIGURES...vi LIST OF TABLES...viii Chapter 1Introduction...1 1.1研究背景...1 1.2研究動機與目的...2 1.3研究成果...3 Chapter 2文獻回顧...4 2.1基於機器學習方式定位方法...4 2.1.1視覺里程計...6 2.1.3地圖座標迴歸...7 2.2定位失效偵測...9 Chapter 3方法架構介紹...11 3.1基礎模型架構...12 3.1.1深度分離卷積...12 3.1.2線性瓶頸結構與反殘差結構...15 3.1.3全域性平均池化層...17 3.1.4姿態迴歸器...18 3.2深度學習模型訓練方法...18 3.2.1幾何限制模組架構...19 3.2.2輔助學習架構...21 3.2.3可學習權重之損失函數...24 3.3蒙地卡羅方法不確定性估測...26 3.4初始姿態估測與定位失效偵測模組...29 3.4.1初始姿態估測...29 3.4.2定位失效偵測演算法...30 3.4.3定位失效參數設計方法...33 Chapter 4實驗結果與討論...38 4.1地圖迴歸座標模型實驗分析...38 4.1.1模型訓練方法...38 4.1.2模型參數量與運算資源比較...39 4.1.3劍橋地標資料集實驗與分析...40 4.1.4七場景資料集實驗與分析...42 4.2輔助定位方法實驗分析...45 4.2.1實驗設備與場域介紹...45 4.2.2初始姿態估測實驗與分析...48 4.2.3定位失效偵測演算法實驗與分析...51 Chapter 5結論與未來建議...61 5.1結論...61 5.2未來建議...61 REFERENCE...62 | |
| dc.language.iso | zh-TW | |
| dc.subject | 卷積深度學習 | zh_TW |
| dc.subject | 視覺定位 | zh_TW |
| dc.subject | 定位失效偵測 | zh_TW |
| dc.subject | Deep Convolutional Neural Network | en |
| dc.subject | Visual Localization System | en |
| dc.subject | Localization Failure Detection | en |
| dc.title | 基於深度學習六維姿態迴歸之自主移動機器人初始姿態估測與定位失效偵測方法 | zh_TW |
| dc.title | Initial localization and localization failure detection for autonomous mobile robots using deep learning based 6D pose regression | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林沛群(Hsin-Tsai Liu),蘇偉儁(Chih-Yang Tseng) | |
| dc.subject.keyword | 視覺定位,定位失效偵測,卷積深度學習, | zh_TW |
| dc.subject.keyword | Visual Localization System,Localization Failure Detection,Deep Convolutional Neural Network, | en |
| dc.relation.page | 68 | |
| dc.identifier.doi | 10.6342/NTU202103161 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-23 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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