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首页> 外文会议>2014 International Symposium on Optomechatronic Technologies >Detecting Discontinuous and Occluded Boundaries from Point Clouds of Building Interiors
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Detecting Discontinuous and Occluded Boundaries from Point Clouds of Building Interiors

机译：从建筑物内部的点云中检测不连续和遮挡的边界

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Range scans of occupied building interiors will often generate a cumulative point cloud with disconnected regions due to varying data density and the presence of numerous partially occluded objects. Boundary detection methods based on surface normal vectors and curvature have difficulty in accurately representing occluded region boundaries because of the geometric uncertainty associated with the underlying polygonal surfaces used to determine the desired geometric parameters. An occluded boundary detection algorithm that works directly on point clouds without the need to reconstruct rough underlying surface models is presented in this paper. The algorithm uses a side-ratio constraint to identify the discontinuous boundary points which lie along successive scan lines. The basic principle is that the distance between the immediate neighboring points at the discontinuous boundary exhibits a large disparity when compared to the other points in a contiguous surface. The side ratio distance defines the spatial separation of the proceeding and succeeding data points on the local grid. The algorithm is also able to handle small density inconsistencies by continuously comparing the side-ratios of the nearest points within a preset window. Spurious point data incorrectly identified as discontinuous boundary points are removed using a density-based outlier detection technique. The effectiveness of the two-step algorithm is demonstrated on real-world data acquired using a FARO® LS 880 laser scanner.
机译：由于数据密度的变化和大量被部分遮挡的物体的存在，对占用的建筑物内部进行范围扫描通常会生成具有不连续区域的累积点云。由于与用于确定所需几何参数的基础多边形表面相关的几何不确定性，基于表面法线矢量和曲率的边界检测方法难以准确表示被遮挡的区域边界。本文提出了一种闭塞边界检测算法，该算法可直接在点云上运行，而无需重建粗糙的下层表面模型。该算法使用侧比约束条件来识别沿连续扫描线分布的不连续边界点。基本原理是，与连续曲面中的其他点相比，不连续边界处的紧邻点之间的距离表现出很大的差异。边比距离定义了本地网格上进行中的数据点和后续的数据点的空间间隔。该算法还可以通过连续比较预设窗口内最近点的边比来处理较小的密度不一致性。使用基于密度的离群值检测技术，可以将错误地标识为不连续边界点的虚假点数据删除。在使用FARO®LS 880激光扫描仪获取的真实数据中证明了两步算法的有效性。

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