請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49992
標題: | 快速場景識別與註冊應用於自主移動機器人六維自由度之全方定位 Fast Scene Recognition and Registration for 6-DoF Global Localization of Autonomous Mobile Robot |
作者: | Vincent Ee Wei Sen 余煒森 |
指導教授: | 羅仁權 |
關鍵字: | 移動機器人,全方定位,場景識別,機器學習, Mobile robotic,Global Localization,Scene Recognition,Machine Learning, |
出版年 : | 2016 |
學位: | 碩士 |
摘要: | 全方定位問題在移動機器人領域中是個必不可少的議題之一,以及成為了重要的部分。大多數的服務型或移動機器人會在室內環境中執行任務。因此,環境的認知成為了機器人不可或缺的條件。若不了解應用環境,機器人可能達不到所指定的位置。雖然全球定位系統 (GPS) 解決了我們迷路的問題,但它只能運用於戶外環境,這導致全方定位在機器人領域中更具有研究價值。
本篇論文提出了一種有效估算機器人姿態的定位演算法。當給予一個大規模的三維點雲 (Point-Cloud) 數據地圖,此演算法能計算出機器人在三維點雲地圖中6維自由度的姿態並且無需事先得知初始位置。我們引入了“快速場景識別與註冊 (Fast Scene Recognition and Registration) ”演算法讓機器人能在三維地圖中定位。我們提出了兩種運算方法分別為FSRRv1與FSRRv2。FSRRv1通過索取子地圖 (Sub-Map) 的多種描述符 (Descriptor) 並把它門串聯起來進行相似度學習 (Similarity Learning) , 以提高場景辨識的準確度。FSRRv2 則添加了影像檢索 (Image Retrieval) 的技術改良原先的定位系統。兩種方法都是通過註冊機器人與子地圖的點雲場景來解出機器人在地圖中的姿態。由於演算法只需匹配機器人與子地圖的點雲場景,故可大量降低運算時間。 我們的技術已被實施,並在不同的建築物廣泛的測試。對於FSRRv1的實驗結果,經過相似度學習後的場景辨識準確率高達90%的精準度。而子地圖的註冊則降低系統的時間複雜度以讓機器人能夠更快速地在三維地圖中定位。對於FSRRv2的實驗結果,Coarse-Pose 只需0.43秒就能計算出機器人可能的粗略的位置。而Exact-Pose 則利用“註冊提煉”更精確地修正機器人在三維地圖中的位置。 實驗結果證明我們的“快速場景識別與註冊”系統可以在各種大型三維點雲數據地圖中有效定位移動機器人。 Global localization problem is one of the essential issue and is a vital part of mobile robot. Most of the service or mobile robot works in the indoor environment to accomplish household tasks. Therefore, the cognitive of environment become the necessary conditions for robot. If without understanding the environment, robots may not reach the specified position. Although GPS and Maps are great, but they only work outdoors and with clear line of sight to the sky. This issue becomes more worthy for robotic research. This thesis describes an algorithm for localization of a robot which can efficiently estimate robot in 6 degrees-of-freedom (DoF) pose which consists of position and orientation with large scale point cloud data without giving the initial pose. We introduce the Fast Scene Recognition and Registration (FSRR) algorithm for robot 6-DoF localization in 3D point cloud map. We propose two different methods, which are FSRRv1 and FSRRv2 approach to solve the robot pose in 3D map. The FSRRv1 algorithm extract Sub-Maps descriptor by cascading several features, and learn a Distance-Metric to increase the precision of place recognition due to the environmental changes. The FSRRv2 algorithm adding image retrieval technique to improve the localization system. Both methods estimate robot pose by point set registration. Our proposed algorithms reduce the computation time needed for the point cloud registration by matching robot’s scene only with the retrieved Sub-Map in database. Our technique has been implemented and tested extensively in different buildings. For the experimental results in FSRRv1 approach, the precision rate of scene recognition over 90% after implemented Similarity Learning. The applied Sub-Map registration was reduce the time complexity to letting robot localize itself in 3D environment more quickly. For the experimental results in FSRRv2 approach, the Coarse-Pose takes only 0.43 second per frame to estimate the possible robot pose on 3D map. The Exact-Pose implement registration refinement in order to revise the robot pose in the 3D map. The experimental results show that our Fast Scene Recognition and Registration system can localize mobile robot in a variety of large scale 3D point cloud dataset efficiently. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49992 |
DOI: | 10.6342/NTU201601979 |
全文授權: | 有償授權 |
顯示於系所單位: | 電機工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf 目前未授權公開取用 | 5.85 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。