本論文提出一套基於雷射掃描匹配的改良式同時定位與地圖建置（SLAM）演算法，並將此演算法系統應用於多機器人地圖建置。在里程估測上，本文利用雷射掃描測距儀（LiDAR）來實現掃描匹配之里程計算，藉此修正傳統里程計資訊，提升定位預測之精確性。在同時定位與地圖建置演算法上，本文提出一套基於Rao-Blackwellized粒子濾波器（RBPF）的改良式SLAM演算法，先利用LiDAR來萃取環境的幾何角點特徵，再透過高信賴度的區域地圖來減少地標點估測時的計算量，使系統具備更佳的效能與強健性。在多機器人SLAM上，本論文提出一套多機器人地圖建置演算流程及系統架構，區域地圖面積做為合併依據，雲端主機會依據群組機器人之區域地圖及羅盤方位資訊，以ICP演算法來合併出全域地圖，提升地圖建置效率。最後，本文將演算法系統於機器人作業系統（ROS）下開發實現，經過移動型機器人於大尺度室內環境中的模擬與實際實驗驗證，本文方法在定位與地圖建置上具有不錯的性能結果，能夠滿足移動機器人探索未知環境的應用需求。 In this dissertation, an improved Simultaneous Localization and Mapping (SLAM) algorithm based on laser scan matching is proposed. This algorithm has also been implemented in mobile robots for multi-robot mapping task. In estimating of odometer, a scan matching algorithm for motion estimation is proposed. It is able to fix modern odometry information which will increase accuracy of localization prediction. In SLAM algorithm, the primary architecture of the algorithm in this dissertation is RBPF which has good performance at estimation. First, using LIDAR to get the features of corner. Then, according to high reliability local map it can decrease calculation when estimating the land marks. By this step, it can let whole system more efficiency and increase the robustness. In the part of mapping by multi-robot, this dissertation will merge local maps. So it will be able to confirm where the robot group is by this fusion map data. However, in the part of merging maps into global map, this dissertation used ICP algorithm to increase mapping efficiency. At last, this dissertation will build whole system under Robot Operating System (ROS). According to simulation and verification by experiment, the method proposed by this dissertation got pretty good performance at localization and mapping which satisfies the requirement of mobile robot exploring under unknown environment.
