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研究生： 李宗霖
Lee, Chung-Lin
論文名稱： 基於漸進線性特徵之RBPF實現同步定位與建構地圖及其於伴隨行走之應用
Piecewise Linear Feature Based RBPF SLAM for Home Service Robot and Its Application to Accompanying Walk
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： 84
中文關鍵詞： RBPF 、同步定位與建立地圖 、漸進線性特徵擷取 、與人伴隨行走
外文關鍵詞： RBPF, SLAM, Piecewise Linear Feature Extraction, Accompanying Walk
相關次數： 點閱：103  下載：2 
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    • 對於居家服務型機器人來說，同步定位與建立地圖是一個十分重要且必須具備之功能。一般而言，如果機器人已擁有精準的環境地圖，那麼定位的功能便易於實現；同樣的如果機器人已經有了準確的定位功能，要建立地圖也並不困難。然同步定位與建立地圖是一個雞生蛋，蛋生雞的問題，機器人必須要同時完成定位與地圖建立。本論文主旨在於提出漸進線性Rao-Blackwellised Particle Filter (RBPF)同步定位與建立RBPF、漸進線性特徵擷取及粒子自我定位演算法。在RBPF架構當中，每一個粒子都擁有自己獨立的一張地圖，因此可以提升地圖的準確度。為了建立更精簡，記憶體消耗更小的地圖，本篇論文採用漸進線性特徵，當作同步定位與建立地圖的特徵，並加入粒子自我定位的演算法，有效減少粒子數，以減少計算資源的消耗並且得到更為準確的估測結果。隨著機器人領域的發展，機器人如何與人互動成為重要的課題，因此機器人與人伴隨行走的方法，成為近年來受歡迎的研究項目。本論文結合了雷射資訊與RGB-D的色彩與深度影像，搭配地圖的資訊，追蹤使用者的位置，並利用泰勒級數展開之速度估測器，估測出使用者移動方向與速度，預測使用者下一步的位置，以維持使用者與居家服務型機器人的相對位置，因應不同地理環境，達成伴隨行走的目的。最後，實驗結果除了展現漸進線性RBPF同步定位與建立地圖方法的精確性，也驗證了與人伴隨行走策略的有效性。

    Simultaneous localization and mapping is an important function for home service robots. Generally, if the environment map is known, then self-localization for a robot is easy to implement. Likewise, if the robot has precise location of its position, establishing the environment map would be facile. However, simultaneous localization and mapping (SLAM) is like the chicken and egg conundrum, the robot has to build map and localize its position at the same time. This thesis presents Piecewise Linear Feature based SLAM (PLFSLAM) method that includes Rao-Blackwellised particle filter (RBPF), piecewise linear feature extraction, and scan matching algorithm. In PLFSLAM, piecewise linear features are extracted as the map features, and each particle carries an individual map. Besides, it adds scan matching algorithm that can reduce the number of particles to achieve accurate estimation of the robot position and build compact map with lower memory consumption. There are many applications of SLAM, in this thesis, we utilize it to implement the accompanying walk with human being. Accompanying walk has become a popular application in human-robot interaction. As known the environment map, the robot can plan a walking path while maintain the relative position with human. A RGB-D sensor is combined with laser information to track the person, and the Taylor series expansion (TSE) velocity estimator is used to estimate the velocity and to predict the pose in next time step of the person. The experimental results demonstrate the accuracy of the proposed PLFSLAM and the effectiveness of accompanying walk with human being.

    摘要 I ABSTRACT II ACKNOWLEDGEMENTS III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VIII LIST OF VARIABLES IX CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 RELATED WORK 2 1.2.1 Simultaneous localization and mapping 2 1.2.2 Human-robot accompanying walk 4 1.3 THESIS ORGANIZATION 4 CHAPTER 2 PIECEWISE LINEAR FEATURE BASED RBPF SLAM 6 2.1 INTRODUCTION 6 2.2 FORMULATION OF SLAM 7 2.2.1 The formulation of SLAM problem 7 2.2.2 Observation model 9 2.2.3 Motion model 12 2.3 RAO-BLACKWELLIZED PARTICLE FILTER FOR SLAM 14 2.3.1 Feature extraction 15 2.3.2 Predict the robot pose by the motion model 21 2.3.3 Rotate and shift 21 2.3.4 Map Matching 22 2.3.5 Estimate the robot pose with scan matching 27 2.3.6 Update map 30 2.3.7 Erase unsighted features 32 2.3.8 Valuate the weight of each particle 32 2.3.9 Resample 33 CHAPTER 3 HUMAN-ROBOT ACCOMPANYING WALK STRATEGIES 34 3.1 INTRODUCTION 34 3.2 HUMAN LEGS DETECTION AND TRACKING WITH THE LASER RANGE FINDER 35 3.3 INTEGRATION WITH KINECT AND LOCALIZATION INFORMATION 38 3.4 PREDICTION OF HUMAN SPEED 40 3.5 MAINTAIN RELATIVE POSITION STRATEGIES 41 3.5.1 Following Strategy 42 3.5.2 Leading Strategy 43 3.6 SIMULATION RESULTS 49 CHAPTER 4 EXPERIMENTS 54 4.1 INTRODUCTION 54 4.2 EXPERIMENTAL SETTING 55 4.2.1 Robot May Ⅱ 55 4.2.2 Experimental environment 57 4.3 EXPERIMENTAL RESULTS OF THE SLAM 58 4.4 EXPERIMENTS OF ACCURACY 62 4.4.1 Experiments of Map Accuracy 62 4.4.2 Experiments of Localization Accuracy 65 4.5 NAVIGATION TASK 67 4.6 EXPERIMENTS OF HUMAN-ROBOT ACCOMPANYING WALK 70 4.6.1 Experiments of following strategy of accompanying walk 70 4.6.2 Experiments of leading strategy of accompanying walk 73 CHAPTER 5 CONCLUSION 77 5.1 CONCLUSION 77 5.2 FUTURE WORKS 78 REFERENCES 79

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