單一相機定位法稱為「MonoSLAM」，雖然使用單一相機的MonoSLAM技術已有相當時間的發展，但在傳統的MonoSLAM理論中因為無法取得正確的特徵點深度資訊，只能假設或使用其他方法推導出其大概的深度距離，因此會影響在做狀態估測時的收斂速度及精確性。
本研究是以測距相機（Microsoft Kinect）作為環境感知感測器，將影像特徵點搭配深度資訊結合，進一步提升影像特徵點匹配的正確率，使得到連續位移影像圖裡相同存在的特徵點，而藉由這些特徵點的位置與相機本身位置的變化，來完成同時定位與建圖（SLAM）的技術，另一方面透過利用測距相機所得到的深度資訊，可以簡化使用擴展式卡門濾波器理論中的狀態方程與其計算時間，並且改善傳統MonoSLAM方法中因影像特徵點無深度資訊造成的定位誤差。

Positioning using single camera is called “MonoSLAM”, although MonoSLAM has been developed for a period of time, traditional MonoSLAM theorem can only assume or use other approaches to derive an approximated depth distance due to incapability of acquiring correct depth information of feature points, therefore affecting the convergence speed and accuracy of state estimation.

This study used Microsoft Kinect as environmental perception sensor to collect depth information, combining with image feature points further improves the accuracy of matching image feature points, obtaining identical feature points existing in a series of images of a continuous movement, achieving SLAM by the variation of the relative position between these feature points and the camera itself, on the other hand using depth information from the depth sensor simplifies the state equation in the Extend Kalman Filter and reduces computational time, improving the positioning error in traditional MonoSLAM caused by the lack of feature point’s depth information.

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