本研究目的為研發一種新的「自我運動估測（ego motion estimation）」技術，技術需求來自於智慧車輛閃避路面障礙物，使用相機來自動偵測前方移動物體，欲將現有任何一種偵測移動物體的方法應用於移動平台上，皆須自我運動資訊。
目前估測方法依據相機系統來分，可分成單相機系統和雙相機系統，經文獻回顧分析後發現欲達成本研究設定目標，深度資訊為不可或缺的資訊，故使用可提供深度資訊的雙相機進行實驗。
本研究提出一完整的估測自我運動演算法。先以速度較快的區域配對技術來做初步配對，再針對配對好的區域中以SSD (Sum of Square Difference)找到對應點以計算點的3D位置。結合這兩種方法來計算深度資訊，特徵點配對的搜尋範圍被大幅縮小，可加快運算速度。
然而因(1)SSD無法避免配對錯誤(2)場景當中有移動物體，以這些對應錯誤的點來估測自我運動，並不正確。本研究設計Truncated Method，以統計的方法來排除對應錯誤的點，經過數次疊代，可得到精確的自我運動參數。
相較於現有未加入深度資訊補償的演算法，只能限定在小範圍內深度變化不大的場景，本演算法可應用在現實中的場景，包括室內及室外，場景當中有移動物體…，皆能計算誤差在兩像素內的移動平台的自我運動，後續可利用此參數做影響補償。而相較於只將SSD window size擴大企圖提高對應正確率的的方法而言，實驗結果證實，本方法能具有較短的運算時間，但卻能估測出更精確自我運動參數。

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