In this thesis we designed and constructed a two-wheel robot vehicle capable of wireless communication with onboard IP camera and motion sensors, the vehicle joins real-time image, measurements from robot’s sensors and robot control command into a local area network, thus achieving cloud controlling with good generalization and expandability between platforms.
As the robot industry turning into a developed market, the complexity of application scenarios and the variety of surrounding factors increases, to enable the robot to perform localization and mapping in various environments, this thesis uses an algorithm to construct 3-D environment model via a single camera, allowing the robot to update its position and build a 3-D point cloud model of the environment while traveling indoor.
This thesis carries out LSD-SLAM ( Large-Scale Direct Monocular SLAM ) on our self-built vehicle, this method can simultaneously reconstruct camera gesture and 3-D model of the environment, reducing possible error in feature extraction method through Semi-Dense Visual Odometry and Pose Graph Optimization, after combining our proposed scale factor correction and coordinate transposition method the estimation results can be presented in real world scale for actual applications after fitting procedure, to further increase computing efficiency we use parallel GPU computation architecture to speed up 3-D environment reconstruction, and reduce both computational complexity and scale shifting by map stitching method, since we adopt a consumer IP camera for image acquisition, we sufficiently reduce the cost of the robot and the size of the image module, giving developers more flexibility with vehicle design.
摘要 I
ABSTRACT II
致謝 III
目錄 VI
圖目錄 VIII
表目錄 XII
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目標 2
1.3 文獻回顧 3
1.4 論文架構 5
第二章 具多軸運動感測器之雲端網路型通用雙輪機器人載具架構 6
2.1 雙輪機器人載具硬體配置 6
2.2 電源配置與電路規劃 13
2.3 雙輪機器人載具控制系統架構與通訊協定 18
2.4 雙輪機器人載具運動模型及控制方法 33
第三章 立體視覺與距離量測 42
3.1 相機幾何 42
3.2 相機參數 44
3.3 相機校正 44
3.4 傳統測距方式 45
第四章 影像同步定位與建圖 49
4.1 同步定位與建圖 49
4.2 MONO-SLAM 49
4.3 LSD-SLAM[36] 51
4.4 比例因子修正與座標轉換 56
第五章 系統建置環境與實驗流程架構 60
5.1 實驗環境 60
5.2 實驗用運算平台軟硬體規格 64
5.3 實驗系統架構 65
5.4 實驗流程 68
第六章 單一相機同步定位與建圖實驗結果 69
6.1 室內環境實驗結果 69
6.2 室內環境實驗結果II 71
6.3 室內環境向上仰視 73
6.4 室外開放空間實驗結果 74
6.5 結果分析 75
第七章 結論與未來展望 78
7.1 結論 78
7.2 相關建議 80
7.3 未來展望 82
參考文獻 83
附錄一 機器人載具元件規格 88
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