近年來，行動機器人導航已應用於無人地面車輛(UGV)、無人表面車輛(USV)、無人機(UAV)。地圖為室內行動機器人導航必要的前提。而在2D空間中即時定位與繪製地圖(SLAM)為一最佳化問題。機器人操作系統(ROS)中已存在許多雷射2D SLAM技術, 適用於不同的情境。對於大型地圖雷射繪製，最俱挑戰性的問題是雷射資訊缺少可以辨認的特徵標誌，特別在走廊中，會造成長度不正確的地圖繪製結果。在機器人導航時，則有可能會發生碰撞。本篇論文提出搭配使用卡慢濾波器的里程計預測應用於掃描匹配，以及回歸線子節點的最佳路徑規畫。將雷射掃描匹配結合經由延伸卡漫濾波器預測之里程計結果。在這樣的方式下，雷射掃描匹配的過程將可以跳離資訊不足的局部極小值，並同時對於里程計資訊有效地做修正。透過迭代修正里程計資訊，匹配結果將會優於過度相信雷射掃描資訊或者里程計資訊。最佳路徑規畫利用a*理論之路徑經由回歸線減少子節點，確保機器人再轉彎時會謹慎而行，在直線時可以快速通過。如此一來，在大型室內場景中行動機器人便可藉由低價雷射測距裝置繪製地圖。將以模擬以及同狀況之室內實驗數據驗證所提出之方法。

In recent years, autonomous robot navigation in indoor environment has successfully been used on unmanned ground vehicles (UGV), unmanned surface vehicles (USV) and unmanned aerial vehicle (UAV). Map is a prerequisite for indoor autonomous robot navigation system. Simultaneous localization and mapping (SLAM) in 2D space is an optimization problem. There has bunch of laser-based 2D SLAM techniques available in Robot Operating System (ROS), and they are fit in different scenario. For large-scale indoor laser scan mapping, a challenging problem is scan information lack of distinguishable landmarks, thus cause the improper mapping result especially in corridor environment. Collision may happen during the autonomous robot navigation. This paper presents a scan matching algorithm with odometer prediction using Extended Kalman Filter (EKF) and an optimal path planning with regression subgoal. The scan matching process will be able to out of local minima, and has an effective correction in the odometry information. By iterating odometer correction in each step, the matching result will be much better than one only believe in scan or odometry. Optimal path planning utilize the a* algorithm with regression method to refined the subgoal amount, ensure the robot will move carefully in the corner and speed up in straight line. Therefore, low-cost laser range finder can be apply to autonomous robot for large-scale indoor environment SLAM. Both simulated and large-scale indoor experimental data under the same conditions are used to verify the effectiveness of the proposed techniques.

論文審定書 i
摘要 ii
ABSTRACT iii
TABLE OF CONTENTS v
LIST OF FIGURES vi
LIST OF TABLES vii
LIST OF AlGORITHMS vii
LIST OF SYMBOLS viii
I. INTRODUCTION 1
1.1 Motivation 1
1.2 Objective 3
1.3 Organization of thesis 5
II. BACKGROUND 6
2.1 Related work 6
2.2 Hector SLAM 9
2.3 Extended Kalman Filter 13
2.4 A* Search Algorithm 15
2.5 Robot Configuration 17
2.5.1 Robot Specification 17
2.5.2 Kinematic of wheeled robot 18
III. PROPOSED METHOD 20
3.1 Proposed Scheme Overview 20
3.2 Odometer EKF with Scan Matching 21
3.3 Path Planning with Subgoal 26
IV. EXPERIMENT RESULT 28
4.1 Environment Setup 28
4.2 Experiment Result 30
V. CONCLUSION AND FUTURE WORK 35
5.1 Conclusion 35
5.2 Future Work 36
REFERENCES 38

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