本研究是以距離感測器做為環境感知感測器的觀點出發，在室內環境中，提出一解決方法即將牆面視為一直線線段表示，故室內環境可視為很多條不同直線線段所代表之特徵牆面，而各特徵牆面所代表之直線方程式皆由不同參數所組合。將載具的位置及牆面直線方程式視為未知狀態，則可發展出一同步定位與環境建圖(SLAM)問題，並可利用載具之運動方程式推導狀態方程式。利用已知角度安裝的距離感測器測量載具到不同牆面的距離，可將距離量測量表為載具位置及特定牆面參數等狀態的非線性函數。

　　利用擴展式卡爾曼濾波器理論，本論文完成載具位置及牆面參數等狀態的估測，針對室內環境實現以距離感測器為基礎之同步定位與環境建圖(SLAM)之技術，同時並在自建的實驗環境下以模擬及實際實驗分別驗證本論文所提方法的可行性，並討論在單一感測器及多感測器量測狀況下的狀態收斂特性。

This research focus on the utilization of range sensor as environment sensing device and describes a new idea to represent the straight walls in indoor environment as straight line segments. The entire environment can then be represented by the set of line segment parameters from all walls . Use the wall parameters and the robot positions as unknown states, a Simultaneous Localization and Mapping (SLAM) problem can be formulated and state equation can be derived by using the robot motion equation. The distance measurements from range sensors installed in fixed angle on the robot to particular wall can be represented as a nonlinear function of the robot position and the wall parameter states.

Using Extended Kalman Filter (EKF) nonlinear estimation theory, the robot position and the wall parameter states can be estimated and an indoor range sensors based SLAM technique is developed. A test environment is built and both simulation and real experiments are conducted to verify the validity of the proposed SLAM method. The state convergence properties using single sensor measurement versus multiple sensor measurements are discussed.

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