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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/396

    Title: 利用RGB-D 攝影機及離散邊緣點之視覺里程計;Sparse Edge Visual Odometry with an RGB-D Camera
    Authors: 許志磊;SYU, JHIH-LEI
    Contributors: 電機工程研究所
    Keywords: 直接法;視覺里程計;關鍵幀;Direct Method;Visual Odometry;Keyframe
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 我們提出了基於彩色與深度圖像(RGB- Method)將特徵匹配與相機運動計算合併進行。我們可以根據光度誤差(Photometric Error)調整光束平差法(Bundle Adjustment)權重以減小離群點產生的影響,然而每個特徵點都會受傳感器的深度噪聲影響,使計算相機姿態時會產生誤差,所以我們使用對極幾何模型與混合分佈模型來更這些特徵點的深度值。根據深度值的更新結果,我們可以使用N點透視法(Perspective-n-Point, PnP) 再次調整相機姿態,減少相機運動在實際運動軌跡上的漂移。我們最後以實驗結果評估本篇方法在修正軌跡的性能與可行性。關鍵詞:視覺里程計、直接法、關鍵幀
    We propose a sparse edge visual odometer model based on the RGB-D camera using direct method to match intenity of the image. Compared with the we use the edge information on the image to keep the robustness of the matching in environment, and merging feature matching into pose estimation. According to photometric error, we can revise the bundle adjustment of weight to reduce the effect of outliers. However, all points is affected by the depth of the sensor noise, that are still producing difference for motion estimations. So we use epipolar geometry and mixture model to update. We will use PnP to adjust pose according to result, and reduce camera motion drift. Finally, evaluate our method in the experiment.Keywords: Visual Odometry, Direct method, Keyframe
    Appears in Collections:[電機工程研究所] 學位論文

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