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研究生：	林毅斌 Lin, Yi-Bin
論文名稱：	整合RGB-D與雷射感測器實現居家服務型機器人之物件資訊探索式SLAM Explored-SLAM with Object Information Using RGB-D and Laser Sensors for Home Service Robot
指導教授：	李祖聖 Li, Tzuu-Hseng S.
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	80
中文關鍵詞：	RGB-D SLAM 、模糊感測器結合、路徑規劃、居家服務型機器人
外文關鍵詞：	RGB-D SLAM, fuzzy sensor fusion, path planning, home service robot
相關次數：	點閱：110 下載：4
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本論文實現居家服務型機器人於居家環境中，可自我探索環境，並同時建立包含物件資訊之視覺特徵地圖與定位。視覺特徵地圖是利用RGB-D攝影機所取得之影像，以SURF及BRISK分別偵測與描述特徵點，並以最小方差法預估該時刻機器人的姿態位置。在建立地圖同時，機器人會將所辨識到的物品或環境資訊，同時標註在地圖上，提升地圖的方便性與實用性。當機器人知道物件的地標位置後，人們將可用更自然的方式給予機器人指令。為了建立完整的視覺地圖，機器人必須具備自我探索的能力，本論文首先利用攝影機的深度資訊，建立2D視覺化地圖，接著利用該地圖避開障礙物產生探索點，再應用蟻群演算法(ACO)規劃所有探索點最短的探訪路徑，然後提出兩階段概率地圖法(Two-stage PRM)，規劃各個探索點間的路徑，使機器人移動更加順暢。為了增進機器人的位置預估精準度，本論文更提出模糊預估法(Multisensory Fuzzy Pose Estimator, MFPE)，整合並調節攝影機(Kinect II)、慣性感測器(IMU)及雷射測距儀三種感測器，進行機器人定位。實驗結果顯示，相較於純視覺定位，結合多種感測器之模糊預估法具備較高的定位精準度。同時，實驗也展示了機器人可同時建立具備物品資訊的視覺地圖，並利用物品地標完成任務。

In this thesis, the home service robot can use RGB-D images and IMU sensors to simultaneous localize and build a map with objects information. For building 3D visual feature map, the robot extracts the features by Speeded Up Robust Features (SURF) and describes the features by Binary Robust Invariant Scalable Keypoints (BRISK). Besides, the Hamming distance is used to match features, and the least square method is used to estimate the pose of the robot. While establishing the map, the robot will recognize objects and mark them on the map. So that the robot will know the position of all recognized objects. To establish the map as complete as possible, a good exploration is necessary. The robot uses the depth information to build a 2D visualized map, and plan exploration points in this map while avoiding all obstacles. Next, the Ant Colony Optimization (ACO) will plan the efficient path to go thought the exploration points. Finally, the thesis proposes two-stage Probabilistic Roadmap Method (TPRM) to plan the path between two exploration points. The TPRM can plan a more smooth and fluent path. To improve the accuracy and stability of localization, the thesis also presents the Multisensory Fuzzy Pose Estimator (MFPE), which integrates the depth camera, IMU, and laser range finder. The experiment shows that comparing with only using depth camera, the accuracy of the map and location can be improved by MFPE. The experiments also demonstrate the efficiency and practicality of building a 3D visual features map with objects landmarks.

Abstract	  I
Acknowledgement	  III
Contents	  IV
List of Figures	  VI
List of Tables	  IX
Chapter 1 Introduction	    1
1	Motivation	    1
2	Related Work  	2
2.1	RGB-D SLAM	    2
2.2	Environment Exploration and Searching	4
2.3	Object Recognition 	5
3	System Overview     6
4	Thesis Organization	                7
Chapter 2 Automatic Exploration and Path Planning	            9
1	Introduction	    9
2	2D Map Visualization	               10
2.1	Registering Obstacles Position	       11
2.2	2D Visualized Map Establishment by Image Processing	   13
2.3	2D Map Updating by Robot Field of View	                   16
3	Robot Exploration Algorithm	       18
4	ACO Path Planning for Exploratory Sequence	           23
5	Two-Stage PRM Path Planning for Robot Moving	           26
5.1	2D Diffused Pollution Map Generation	                   26
5.2	Conventional PRM Method	               29
5.3	Dijkstra’s Algorithm Implementation	                   31
5.4	Proposed Two-Stage PRM Method	       34
Chapter 3 Integration SLAM and Object Information	           37
1	Introduction	   37
2	RGB-D SLAM	   38
2.1	SLAM System Structure	               38
2.2	Feature Extraction	               40
2.2.1	SURF and BRISK	               40
2.2.2	Feature Refinement             44
2.3	Feature Matching and Pose Estimation	                   45
2.4	Feature Map Updating	               48
3	Fuzzy Based Multisensory Pose Estimator	                   50
4	Object and Landmark Tagging	       55
4.1	Object Recognition	               55
4.2	Object Tagging	   57
Chapter 4 Experiments	   58
1	Introduction	   58
2	Experimental Setting	               59
2.1	Environment	   59
2.2	Home Service Robot	               60
3	Experiment I：Explored-SLAM	       61
4	Experiment II：Test the Accuracy of Localization	           64
5	Experiment III：Object Searching and Grasping with Object Information Map      70
Chapter 5 Conclusion and Future Works	       72
1	Conclusion	   72
2	Future Works	   73
References	           75
                                    

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校內：2022-07-01公開
校外：2022-07-01公開

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