本論文主要在探討居家服務型機器人之物件辨識系統與視覺感知功能的設計與實現。首先介紹結合CUDA SURF特徵點偵測法和BRISK特徵點描述法來實現的物件辨識系統，其中CUDA SURF是利用GPU平行化處理來加快原始SURF的速度，而BRISK與傳統的特徵描述法不同，採用了二進位字串而非浮點數來記錄特徵值，這樣的方法不但減少了容量的消耗，同時也加快了比對結果的速度。針對尋找不在資料庫內的物品，我們提出了一種視覺感知系統，首先從彩色影像中偵測邊緣，接著計算該邊緣附近的深度資訊，來產生其是否為物體邊界的機率，再找出物體的輪廓。本實驗室研究開發的人機介面包含兩個部分，針對開發者，介面應設計的簡潔易懂；對於使用者，語音辨識及手勢辨識系統的設計，使其能夠輕鬆地傳達指令或是取得機器人的注意。最後，將以上所提出的方法應用於居家服務型機器人，並透過實驗室的實驗結果與2013 RoboCup@Home日本公開賽的比賽成果證明該即時物件辨識系統其可行性與實用性。

This thesis mainly discusses the design and implementation of object recognition and visual perception for home service robot. In the beginning, the object recognition system built by combining Compute Unified Device Architecture (CUDA) Speeded Up Robust Features (SURF) detector and Binary Robust Invariant Scalable Keypoints (BRISK) descriptor is presented. CUDA SURF improves the speed of original SURF through Graphic Processing Unit (GPU) parallelization. Unlike traditional descriptors, BRISK uses the binary string instead of floating vector to store the description. It not only decreases the consumption on memory but also reduces the computation time for matching. Next, we propose a visual perception system for searching objects, which are not included in database. By calculating the depth difference between two sides of an edge, a probability weight is generated to evaluate whether it is a boundary or not. The contours of objects are searched according to the probability. Then, the Human-Computer Interaction (HCI) is introduced in two ways: for developers, these interfaces should be clear and easy to understand, and for operators, the designs of speech recognition system and gesture recognition system make it convenient to convey instruction or get the attention from robot. In the end, these methods mentioned above are implemented in the laboratory and the competition in robot@home league at RoboCup Japan Open 2013 Tokyo, and the validity and practicability of this real-time object recognition system are demonstrated.

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