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城市场景中视觉SLAM地图优化研究
其他题名A Study on Map Optimization of Visual SLAM Systems in Urban Environments
谢远帆
学位类型工学博士
导师吴毅红
2013-12-27
学位授予单位中国科学院大学
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词地图优化 视觉slam 捆绑调整 图划分 Map Optimization Visual Slam Bundle Adjustment Graph Partition
摘要随着信息技术的高速发展和海量多媒体数据的不断涌现,视觉同步定位与地图创建(Simultaneous Localization and Mapping, SLAM)技术逐渐成为了导航、增强现实与城市街景建模等诸多应用的核心。尽管已有文献对视觉SLAM技术展开了广泛的研究,然而视觉SLAM地图精度问题和地图优化效率问题仍然没有得到有效解决。这严重影响了视 觉SLAM系统在城市环境中的应用水平。针对上述两个问题,本文展开了较为系统深入的研究,主要贡献有: (1) 提出了一种融合场景结构信息的地图数据优化方法。该方法利用城市场景结构特点,首先自动提取出场景结构信息,之后在保证优化问题可稀疏求解的前提下,采用参数化方式将结构信息融合在地图数据的表示当中,并通过一种具有结构不变性的改进捆绑调整算法对整体地图数据进行优化,从而利用先验信息提升系统精度。实验表明, 随着先验结构信息的加入,三维结构和相机运动参数的估计精度都有所提升。这种提升在视数较少、相机运动基线较短的情况下尤为显著。 (2) 提出了一种基于图划分的子地图化捆绑调整方法。该方法对图像间匹配关系建立一个无向图,并以此对地图数据各部分的局部性质进行表示。在此基础上,该方法针对城市环境大规模地图数据的稀疏性,首先利用图划分的方法将整体数据自上而下划分得 到具有二叉树结构的子地图,然后对各子地图分别进行优化并采用自下而上的方式融合。通过将整体优化问题分解为局部优化问题和融合问题,该方法在保证了一定优化精度的同时提高了整体地图优化效率。实验表明该方法在地图数据稀疏结构保持不变并且相机数目很大时,其时间复杂度与相机个数呈线性关系,相对于立方复杂度的整 体数据捆绑调整来说具有更大的时间优势。 (3) 提出了一系列适用于视觉SLAM计算框架的模块化算法,包括地图优化算法、地图创建算法和视觉定位算法,并在此基础上设计了一个适用于城市环境的视觉SLAM原型系统。该系统的地图优化模块引入了融合结构信息的优化算法以利用先验信息提升结构估计精度。此外,地图创建模块引入了延迟的创建方式,提高了地图创建的精度 和鲁棒性。更进一步地,视觉定位模块对像素特征进行局部仿射变换以提高图像特征匹配率,达到了鲁棒实时定位的效果。对比实验表明,集成上述模块化算法的视觉SLAM系统具有较高的地图精度、定位精度和鲁棒性。
其他摘要With the rapid development of information technology and the proliferation of multimedia data, visual Simultaneous Localization and Mapping(SLAM) technology is gradually becoming a core technology for many applications, such as visual navigation, augmented reality, and street view modeling etc. Although comprehensive studies on visual SLAM systems have been undertaken, two key problems, the accuracy of mapping and the efficiency of map optimization, have been not resolved completely yet, which severely degrade the level of application of visual SLAM in an urban environment. Addressed these two problems, a systematical research has been conducted. The main contributions are: (1) A new map optimization method, which incorporates prior knowledge of an urban structure, is proposed. The proposed method first extracts the structured regions of a map, and then incorporates these structures by means of a new way of parameterization where the sparsity is preserved. Subsequently, a new sparse bundle adjustment method is present, which preserves the geometric invariance of these structured parts, and thus the accuracy of the built map is improved. Experimental results show that both the map and the camera motion are recovered with higher accuracy in the presence of prior information, compared with the methods incorporating no prior knowledge. The improvement is especially significant in the cases where only a few views are available or the baselines among the views are narrow. (2) A graph-partition-based bundle adjustment method is proposed, which exploits the sparsity of large scale maps of urban applications. By representing the locality of all the extracted local regions of the whole map with an undirected graph induced from the matched features among images. First, via the multi-level graph partition technique, the whole map is recursively divided into submaps in a top-down manner, which finally leads to a binary tree representation. Then all submaps are optimized individually, and merged in a bottom-up manner. By optimizing and merging submaps rather than optimizing the whole map, the proposed method achieves higher efficiency and comparative accuracy compared with existing bundle adjustment methods. Experimental results show that if the sparse structure of the map remains unchanged and the number of cameras in the map is large, the computational cost of the proposed method is proportional to the number of cameras, which is superior to the whole map optimiza...
馆藏号XWLW1950
其他标识符200918014628059
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6575
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
谢远帆. 城市场景中视觉SLAM地图优化研究[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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