- 學位論文
基於單眼視頻低梯度區域深度估計之稠密片段平面地圖重建
Dense Piecewise Planar Reconstruction based on Low Gradient Region Depth Estimation from a Monocular Image Sequence
摘要
近幾年來，機器人自主導航是一個熱門且具有挑戰的主題，這項技術可以應用在環境的探勘或是運送貨物。在機器人自主導航中環境感知是一個重要的一環，特別是在GPS信號被遮蔽區域的室內環境中，本篇利用機器人上的相機錄製的視頻透過LSD SLAM[11: Engel et al. 2014]去計算每個關鍵偵的深度資訊，將其關鍵偵的RGB影像及深度圖抽出，利用區域增長的方式將紋理梯度較低的區域抽取出來，並且基於人造的環境紋理較低的區域大部分都是平面的假設，提出一個深度填補的方法，優化每個關鍵偵的深度地圖的稠密度，提供機器人更豐富的環境資訊在自主導航應用上面，在單目SLAM遇到的尺度問題，本篇使用環境中已知的物件去計算尺度，並利用計算得到的尺度去定義深度填補中的閥值去濾除不合理的填補， 本文利用Gazebo 模擬器[35: Gazebo from OSRF, Inc] 慕尼黑官方數據集[23: Sturm et al. 2012]和實驗使用微軟Kinect傳感器針對幾種方法做了比較 比較結果顯示本篇深度填補的方法在單眼SLAM片段平面上比LSD SLAM[11: Engel et al. 2014] 和DPPTAM[12: Concha & Civera 2015]更加稠密。
並列摘要
Visual navigation of robot has been a popular and a challenge research topic in past few years. One of important part for navigation is environment sensing. Especially for previously unknown and GPS-denied environments, this thesis uses monocular camera to obtain image data and estimates the depth map information in each keyframe by LSD SLAM [11: Engel et al. 2014]. RGB image and depth map in each keyframe are extracted to detect low texture regions by region growing segmentation method. The assumption made is that image areas with low photometric gradients are mostly planar which is met in most indoors and man-made scene. This thesis proposes a depth filling method to optimize the depth map completeness in each keyframe. It can provide robot more environment information to apply on navigation. For monocular unknown scalar problem, the assigned marker in the scene is used to compute the scale. However, the estimated scale is used to define the thresholds that are used to filter out the unreasonable plane estimation in depth filling process. This thesis compares the depth filling method against several alternatives using Gazebo simulation [35: Gazebo from OSRF, Inc], public Tum dataset [23: Sturm et al. 2012], and experiment with a Microsoft Kinect sensor. The comparison demonstrate that our depth filling method for piecewise planar monocular SLAM is denser than LSD SLAM [11: Engel et al. 2014] and DPPTAM [12: Concha & Civera 2015].
參考文獻
延伸閱讀
- Chen, Y. T. (2016). 基於片狀平面約束與高斯牛頓法進行單眼影像序列之正規半稠密環境重建 [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201602272
- Chuang, Y. C. (2019). 基於單目視覺與稀疏深度資訊之高效深度估測網路 [master's thesis, National Chiao Tung University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0030-0306202016350587
- YONG, L. S. (2015). 整合高階模糊派翠網路和特徵點比對之分鏡自動偵測 [master's thesis, National Taipei Uinversity]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0023-1005201615094504
- Chen, W. C., Chen, Z., & Sung, P. Y. (2015). Stochastic Optimization Based 3D Dense Reconstruction from Multiple Views with High Accuracy and Completeness. Journal of Information Science and Engineering, 31(1), 131-146. https://doi.org/10.6688/JISE.2015.31.1.7
- Chen, W. C. (2014). Stochastic Optimization Based 3D Dense Reconstruction from Multiple Views with High Accuracy and Completeness [doctoral dissertation, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2014.00776