同步定位與建圖 (Simultaneous Localization and Mapping，SLAM)為近年來最熱門的話題，室內移動機器人可以透過感測器來獲得環境資訊，使機器人在環境中能得知自身的位置，並且建立室內地圖，來達到搬運貨物、打掃環境等功能，應用層面之廣泛在未來的科技上也令人期待。
通常這樣的技術需搭配高準確度的感測器，如LiDAR，但此感測器價格昂貴，導致產品本身的成本提高，且LiDAR體積偏大，安裝時佔據產品一部分的空間。
因此，本論文將架設一套低成本的驅動車系統，使用ROS系統搭配Gmapping並基於RBPF粒子濾波器演算法 (Rao-Blackwellized Particle Filter，RBPF)建立室內地圖。此系統使用八個ToF感測器 (Time of Flight，ToF)來獲取環境距離資訊，並利用EKF擴展型卡爾曼濾波器 (Extended Kalman filter，EKF)融合馬達編碼器的里程與IMU感測器(Inertial Measurement Unit，IMU)的數據，以更準確的評估驅動車的位置和建立環境地圖。
此系統在一個靜態環境定點建圖和兩個動態環境即時建圖中進行驗證。靜態定點建圖部分，本系統建圖的結果和實際地圖相近。動態即時建圖部分，實驗結果顯示有使用擴展型卡爾曼濾波器的建圖結果優於沒有使用擴展型卡爾曼濾波器的建圖結果。

Simultaneous Localization and Mapping (SLAM) is a hot topic in recent years. Indoor mobile robots obtain environmental information through sensors to know its position in the environment and to build indoor maps, and thus can be used to transport goods or clean the environment. More applications of the SLAM technology in the future is to be expected.
The SLAM technology usually equips with high-accuracy sensors, such as LiDAR. However, these sensors are expensive and thus will increase the relative costs on providing. Moreover, the volume of LiDAR is so large that occupies a volume of product space.
Therefore, in this paper, we set up a low-cost driving vehicle with the ROS system using Gmapping based on RBPF (Rao-Blackwellized Particle Filter, RBPF) algorithm to build an indoor map. This system uses eight ToF sensors (Time of Flight, ToF) to get the information on environmental distance, and uses the EKF (Extended Kalman filter, EKF) to fuse the encoder's mileage with the IMU sensor (Inertial Measurement Unit, IMU) data to more accurately estimate the positioning of the driving vehicle and to build an environmental map.
This system is verified in a static environment with fixed-point mapping and two dynamic environments with real-time mapping. In the static fixed-point mapping, the result of the system’s mapping is similar to the real map. In the dynamic real-time mapping, the experimental results show that the mapping using the Extended Kalman Filter is better than the mapping not using the Extended Kalman Filter.

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