王傑智臺灣大學：資訊工程學研究所陳泰良Chen, Tai-LiangTai-LiangChen2007-11-262018-07-052007-11-262018-07-052007http://ntur.lib.ntu.edu.tw//handle/246246/54061在動態環境中，強健地機器人位置估測是移動物體偵測以及追蹤的重要先決條件。由於分裂與群集的影響，單一的資料分割並不足以表達真實環境中的物體。這類的問題可能會造成錯誤的資料關聯性。為了解決此類問題，我們提出了使用取樣與關聯性為基礎之距離影像比對演算法之合併區段法。考慮所有合併區段組合之不確定性，在資料關聯性與資料分割上找出可能的假設。我們也詳述了從感測資料、區段、分割、資料關聯性到移動物體偵測與追蹤之所有不確定性。我們解決了真實環境中的問題，例如車子的上下振動，以及在動態環境中的定位與移動物體偵測。Robust robot pose estimation is an important prerequisite of moving object detection and tracking in dynamic environments. Since a simple segmentation is not enough for the representation of an object due to problem of fragmentation and grouping of the segments. It may cause incorrect data association. To address these problems, we propose a segment-merging approach with the Sampling- and Correlation-based Range Image Matching(or SCRIM) algorithm. To concern about the uncertainty of the combination of all the merged segments, we find all the proper and possible hypothesis of data association and segmentation. We specify all the uncertainty from the measurement, segment, segmentation and data association, to detection and localization. Also we address the problems such as pitch motion of the robot, localization and detection in dynamic environments.ABSTRACT ..... ii LIST OF FIGURES ..... iv LIST OF TABLES ..... v CHAPTER 1. Introduction ..... 1 CHAPTER 2. Related Work ...... 3 CHAPTER 3. Segmentation.....5 3.1. Segment-merging and Segmentation ..... 5 3.2. SCRIM..... 6 Sampling..... 6 Correlation ..... 6 CHAPTER 4. Localization and Moving Object Detection ..... 14 4.1. Statistical Motion Analysis ..... 15 4.2. Localization and Moving Object Detection from the Hierarchical Uncertainty Scheme ..... 18 CHAPTER 5. Experiment Results..... 30 CHAPTER 6. Conclusion and FutureWork ..... 38 BIBLIOGRAPHY ..... 39 Index .....411564926 bytesapplication/pdfen-US強健移動物體偵測定位機器人robustmoving objectdetectionlocalizationthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/54061/1/ntu-96-R94922069-1.pdf