結合二維影像與三維點雲資訊之物體辨識

Abstract

近年來，在辨識三維物體的研究中，點雲資料逐漸成為重要的研究對象。在 不同的拍攝視角與不同的拍攝距離所選取的特徵，往往會直接影響辨識的成效。 為了解決這個問題，本論文提出一個整合型辨識系統，由不同距離所得到不同完 整度的影像，利用辨識策略的切換以達成兼顧不同距離時的辨識。在遠距離時， 採用傅立葉描述子作為形狀描述的特徵，以及視角內插法來提升辨識的正確率； 當距離足夠近時，由於塔台的輪廓線會越來越不具代表性，造成辨識的錯誤率增 加，故本論文採用一個結構描述子做為辨識時所使用的特徵，以點雲資料直接進 行辨識。本論文以十個不同的塔台模型

In recent years, research works of three dimensional object recognition in point cloud data become more and more popular. Appearance-based features, such as silhouettes of objects, will directly affect the recognition efficiency in different positions with various angles. To tackle this problem, this thesis proposes a recognition system with two-feature integration. One is the Fourier descriptor of the contour in a range image, and the other is the structure descriptor extracted from point clouds. The Fourier descriptor is used to identify an object in the far distance. Additionally, a method of view-angle interpolation is proposed to increase the correct recognition rate. The structure descriptor is used to recognize an object when closing to the object, since the contour information lacks the ability to describe the object. Furthermore, a strategy of proposed method is presented to select the appropriate feature for object recognition. Ten different control towers are used to verify the performance of the proposed approach. The experimental results show that the proposed system performs better than the method using only feature of range image or feature of point clouds data across the entire distance range.