本論文的目的在於提高基於雙眼攝影機之視覺里程計準確性與即時性。本論文使用加速強健特徵(speeded up robust features,SURF)演算法擷取環境地標，並使用三點透視(perspective-3-point, P3P)以及隨機取樣一致(random sample consensus, RANSAC)演算法來定位機器人的位置。在本論文中使用直立式加速強健特徵(upright speeded-up robust features, U-SURF)、限制盒子濾波器的門閥值、參數化三點透視以及隨機化隨機取樣一致(Randomized random sample consensus, R-RANSAC)來降低視覺里程計的計算時間。藉由高效率加速強健特徵的較高計算次數進而提高準確性。同時參數化三點透視亦可以提高視覺里程計的準確性。改善的演算法將實現在二輪差速驅動機器人之上。 The purpose of this thesis is to improve the accuracy and instantaneity of the binocular-camera-based visual odometry (VO). The speeded-up robust features (SURF) algorithm was used to capture environment landmarks, and the perspective-3-point(P3P) algorithm and random sample consensus (RANSAC) method were used to the robot localization. The Upright-SURF, threshold of box filter, novel parametrizated P3P algorithm and randomized random sample consensus (R-RANSAC) algorithm were used to reduce computing time of the VO. The revised SURF can increase the frequency of landmark localization and then improve accuracy of the VO. The novel parametrizated P3P algorithm also improves accuracy of the VO. The improved VO was implemented on the two-wheeled mobile robot to verify accuracy and instantaneity of the VO.