物件辨識和姿態估計對場景理解和高效，靈活和可靠的自主系統而言是一項關鍵任務。傳統上，物件辨識的大多數研究工作都集中對2D影像中物件，使用包括雜波、遮擋和不同的照明場景的內容進行檢測和分類。使用機器學習方法進行物件辨識，儘管具有很高的強健性，但這些方法都是從2D視角來面對問題，而不是在3D空間中考慮物件的精確旋轉方位和位置。在此背景下，根據3D場景數據的方法作為第一種可針對不同的複雜場景穩健地解決6D姿態估算問題的解答方案，顯示出迄今為止最可期待的性能水平和最佳的結果。然而，問題尚未解決，高度混亂的場景和遮擋仍然是這最先進方法的具體挑戰。本文提出並分析了基於表現最佳的點對特徵投票(point pairs features voting)方法的創新解決方案，以定義一種創新的以特徵為基的方法，用於在雜亂場景下對部分遮擋物件進行強健辨識和6D姿態估計。
此研究考慮了當前方法的缺點後，定義出新的判別預處理方法、改進的匹配方法、更強健的群聚和一些跟視角相關的後處理步驟。針對具有挑戰性的物件阻擋場景，本研究還提出了一種基於自上而下的視覺注意力和色彩提示的創新解決方法，以提高在物件僅部分可被看見案例之性能。本論文所提出的方法的性能是和14種目前最先進的方法，在一個最全面且公開可得，具雜亂和遮擋的真實場景之基準下一起進行評估。結果顯示，本文提出的方法在所有資料庫上的性能均明顯優於所有一起測試的最先進解決方案。本方法在不同類型的物件和場景皆展示了有效性，特別是在相對低的物件可見情況下提高了性能，擴展了當前6D姿勢估計方法的能量。最後，通過建構和測試一種用於智慧製造的創新自動離線編程解決方案，證明了本研究的實用價值。具體地說，建構了一套自動機器手臂整合系統，用以充分發掘物件辨識和姿態估計技術的強健性和進步性。物件辨識方法先將工件姿態資訊提供給靈活的離線編程平台，以全自主方式有效地解決在製造場景中機器手臂整合的關鍵問題。本系統在真實場景的一系列實驗中進行測試，並與不同的現有解決方案進行比較，顯示出本方法的穩健性和優勢。總體而言，該整合系統顯示了本文提出之最尖端物體辨識方法的價值和潛力，定義出針對高度先進全自主系統的創新智慧解決方案。

Object recognition and pose estimation is a crucial task towards scene understanding and highly efficient, flexible and reliable autonomous systems. Traditionally, most research efforts in object recognition have been focused on the detection and classification of objects in two-dimensional images, including clutter, occlusion and different illumination scenarios. Despite reaching a high level of robustness, specially using machine learning approaches, these methods face the problem from a 2D point of view rather than providing the precise rotation and position of the objects in the 3D space. In this context, methods based on three-dimensional scene data appeared as the first solutions to robustly solve this 6D pose estimation problem for different complex scenarios, showing a promising level of performance with the best results so far. However, the problem has not yet been solved, with highly cluttered scenes and occlusions still remaining challenging cases for state-of-the-art methods. This thesis proposes and analyses novel solutions based on the top performing Point Pair Features voting approach to define a novel feature-based method for robust recognition and 6D pose estimation of partially occluded objects in cluttered scenarios.
The research considers the drawbacks of current approaches to define a novel discriminative preprocessing solution, an improved matching method, a more robust clustering and several view-dependent postprocessing steps. Focusing on the challenging occluded cases, the research also proposes a innovative solution based on top-down visual attention and color cues to boost performance in partially visible cases. The performance of the proposed method is evaluated against 14 state-of-the-art solutions on a comprehensive publicly available benchmark with real-world scenarios under clutter and occlusion. The results shows an outstanding improvement for all datasets outperforming all tested state-of-the-art solutions. The validity of the proposed approach is shown for different types of objects and scenarios, specially boosting performance for relatively low visible cases extending the capacities of current 6D pose estimation methods. Finally, the practical value of the research is demonstrated by defining and testing a novel automatic offline programming solution for intelligent manufacturing. Specifically, an automatic robot integration system that exploits the robustness and benefits of the recognition and pose estimation is proposed. The recognition method provides the workpiece pose information to a flexible offline programming platform, efficiently solving, in an autonomous way, a critical problem for robot integration in manufacturing scenarios. The system is tested on a series of experiments on real-world scenarios and compared against different existing solutions, showing the robustness and benefits of the method. Overall, the system shows the value and potential of a cutting edge object recognition method to define innovative intelligent solutions towards highly advanced autonomous systems.

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