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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 連豊力(Feng-Li Lian) | |
| dc.contributor.author | Chung-En Liu | en |
| dc.contributor.author | 劉仲恩 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:29:48Z | - |
| dc.date.available | 2020-08-21 | |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-10 | |
| dc.identifier.citation | Vishakh Duggal, Mohak Sukhwani, Kumar Bipin, G. Syamasundar Reddy, K. 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51298 | - |
| dc.description.abstract | 本篇論文為無人載具主動同步定位與地圖建構之系統，透過主動找尋最佳視野方向的方式，降低單眼視覺同步定位與地圖建構之定位誤差，並建立多解析度分析地圖。在此系統中，無人載具倚賴單眼視覺慣性達成定位，並將角點偵測影像傳回電腦以求出下一個最佳的視角方向來減少定位誤差。多解析度分析地圖是基於結合定位以及彩色深度攝影機的點雲資訊建立的。本篇論文的貢獻在於，藉由最佳化下一個視角來提升無人載具在無特定結構環境中的定位準確度並建立一個基於周圍物體距離的多解析度分析地圖。 整套系統由六個平行運算的程序組成。VINS-Mono程序藉由彩色影像和慣性測量資訊提供無人載具的定位資訊。RealSense程序負責提供周邊環境的點雲資訊。Octomap程序將定位資訊和不同解析度的點雲資訊結合在一起，以建立多解析度分析地圖。感知意識導航程序藉由求出下一個最佳化的視角，提升無人載具在無特定結構環境中的定位準確度。位置控制器藉由提供基於無人載具和給定終點的距離成比例的速度。偏擺控制器界定可以轉動的範圍並負責提供偏擺的速度。 最後，本論文透過實驗結果來展示此系統的可行性以及性能。 | zh_TW |
| dc.description.abstract | In this thesis, an active monocular SLAM system for a mobile robot is proposed with an aim to minimize pose estimation errors and construct a multi-resolution map of the surrounding environment. In this system, the system uses visual-inertial odometry for localization and further processes the feature detection image to compute the best viewpoint for pose error minimization. The multi-resolution map is constructed by fusing pose estimations with pointcloud from an RGB-D camera. The key contributions of this thesis include improved mobile robot localization in poorly textured environments through viewpoint optimization and detailed environment reconstruction while considering the distance of the surrounding objects. The system consists of six programs running parallel, each acting individually but play an important role in either localization or mapping. The VINS-Mono program localizes the robot by fusing IMU measurements with RGB images. Pointclouds of the surrounding environment, with decreasing resolution for objects further away from the robot, are output from the RealSense program and further combined with VINS-Mono localization for map construction. The Octomap program fuses octomaps of different resolution and probabilities to generate a probabilistic map of the navigation space with respect of object distance and hazardous. The Perception Aware Strategy program equips the system with the ability of minimizing pose errors while navigating in textureless environments. A next-best viewpoint is continuously generated based on the number and position of the detected features. The pose controller program provides the robot with velocities proportionate to the untraveled distances while considering the effects of high velocities on visual-inertial pose estimation. The yaw controller program outputs angular velocities to the robot and bounds the view of the robot with camera parameters to prevent increasing pose estimation errors due to excessive orientation. Analysis on experiments is provided to demonstrate the effectiveness of the proposed system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:29:48Z (GMT). No. of bitstreams: 1 U0001-1008202012513300.pdf: 18069263 bytes, checksum: e59b2f0d48d4f89528fb5832f6222e21 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 摘要 i ABSTRACT iii CONTENTS vi LIST OF FIGURES viii LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Motivation 2 1.2 Problem Formulation 4 1.2.1 Vehicle Localization in 3D space 5 1.2.2 Perception Aware Navigation 6 1.2.3 Multi-resolution Map Construction 6 1.3 Contributions 7 1.4 Organization of the Thesis 9 Chapter 2 Background and Literature Survey 11 2.1 Simultaneous Localization and Mapping (SLAM) 11 2.2 Localization 12 2.2.1 Visual-Inertial Odometry 13 2.3 Mapping 16 2.4 Active SLAM 18 Chapter 3 Related Algorithms 20 3.1 Pinhole Camera Model 20 3.2 Monocular Visual-Inertial State Estimator (VINS-Mono) 23 3.3 Octomap 28 3.4 K-means Clustering 30 Chapter 4 System Overview 31 4.1 Coordinate Frames 31 4.1.1 The VINS-Mono Coordinate Frame 31 4.1.2 The Octomap Coordinate Frame 33 4.1.3 The Turtlebot Coordinate Frame 34 4.2 System Architecture 36 Chapter 5 Perception Aware Navigation 40 5.1 Perception Aware Viewpoint Planning 40 5.1.1 Ground Removal 41 5.1.2 Viewpoint Optimization Strategy 45 5.1.3 Maximum Orientation Policy 50 5.2 Vehicle Navigation 52 5.2.1 Waypoints 53 5.2.2 Position Controller 55 5.2.3 Yaw Controller 57 Chapter 6 Multi-minimum Resolution Octomaps 59 6.1 Single Minimum Resolution Octomap Construction 59 6.2 Filtering and Map Merging 61 Chapter 7 Experimental Results and Analysis 64 7.1 Experimental Setup 64 7.1.1 Hardware Platform 64 7.1.2 Software Platform 69 7.2 Perception Aware Navigation with the Turtlebot Mecanum 70 7.2.1 Long-narrow Corridor Scenario 76 7.2.2 Open Region with Diagonal Trajectories 100 7.2.3 Curved Trajectories 139 7.2.4 Extreme Handcrafted Scenario 187 7.3 Multi-resolution Maps 211 7.3.1 Resolution and Map size 211 7.3.2 Static and Dynamic Environments 215 7.3.3 Exploration SLAM Results 217 7.4 Summary 237 7.4.1 Long-narrow Corridor Scenario 237 7.4.2 Open Region with Diagonal Trajectories 239 7.4.3 Curve Trajectories 241 7.4.4 Extreme Handcrafted Scenario 242 7.4.5 Multi-resolution Maps 244 Chapter 8 Conclusions and Future Works 246 8.1 Conclusions 246 8.2 Future Works 248 References 251 | |
| dc.language.iso | en | |
| dc.subject | 定位 | zh_TW |
| dc.subject | 單眼視覺同步定位與地圖建構 | zh_TW |
| dc.subject | 主動同步定位與地圖建構 | zh_TW |
| dc.subject | 定位 | zh_TW |
| dc.subject | 多解析度分析地圖 | zh_TW |
| dc.subject | 感知意識導航 | zh_TW |
| dc.subject | 主動同步定位與地圖建構 | zh_TW |
| dc.subject | 無人載具 | zh_TW |
| dc.subject | 無人載具 | zh_TW |
| dc.subject | 感知意識導航 | zh_TW |
| dc.subject | 多解析度分析地圖 | zh_TW |
| dc.subject | 單眼視覺同步定位與地圖建構 | zh_TW |
| dc.subject | mobile robot | en |
| dc.subject | Monocular SLAM | en |
| dc.subject | active SLAM | en |
| dc.subject | localization | en |
| dc.subject | multi-resolution map | en |
| dc.subject | perception aware navigation | en |
| dc.subject | mobile robot | en |
| dc.subject | Monocular SLAM | en |
| dc.subject | active SLAM | en |
| dc.subject | localization | en |
| dc.subject | multi-resolution map | en |
| dc.subject | perception aware navigation | en |
| dc.title | 基於單眼視覺慣性與環境感知之無人載具環境探索與地圖建立 | zh_TW |
| dc.title | Monocular Visual-Inertial-Based Navigation for Mobile Robots with Perception Aware Exploration and Map Construction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李後燦(Hou-Tsan Lee),黃正民(Cheng-Ming Huang),許志明(Chih-Ming Hsu) | |
| dc.subject.keyword | 主動同步定位與地圖建構,單眼視覺同步定位與地圖建構,定位,多解析度分析地圖,感知意識導航,無人載具, | zh_TW |
| dc.subject.keyword | Monocular SLAM,active SLAM,localization,multi-resolution map,perception aware navigation,mobile robot, | en |
| dc.relation.page | 257 | |
| dc.identifier.doi | 10.6342/NTU202002787 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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