NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation.
NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation.
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DOI：
10.1109/tcyb.2014.2316282
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发表时间：
2014-12
影响因子：
11.8
通讯作者：
Ye C
中科院分区：
文献类型：
--
作者：
Qian X;Ye C
本文提出了一种基于随机样本共识（RANSAC）方法的新平面提取（PE）方法。基于 RANSAC 的通用 PE 算法可能会过度提取平面，并且在多步骤场景的情况下可能会失败，其中 RANSAC 过程会导致形成跨步骤的倾斜平面的多个内点补丁。如果内点补丁是分离的，CC-RANSAC PE 算法成功地克服了后一个限制。但是，如果连接了内部补丁，则会失败。一个典型的场景是带有楼梯墙的楼梯，其中 RANSAC 平面拟合过程会在踏面、竖板和楼梯墙平面中产生内层补丁。它们连接在一起并形成一个平面。所提出的方法称为法线相干 CC-RANSAC (NCC-RANSAC)，对内点补丁的所有数据点执行法线相干检查，并删除法线方向与拟合平面方向相反的数据点。此过程会产生单独的内点补丁，每个内点补丁都被视为候选平面。然后执行递归平面聚类过程来增长每个候选平面，直到完整提取所有平面。重复 RANSAC 平面拟合和递归平面聚类过程，直到找不到更多平面为止。引入概率模型来预测 NCC-RANSAC 算法的成功概率，并使用 3D 飞行时间相机 SwissRanger SR4000 的真实数据进行验证。实验结果表明，与现有基于 RANSAC 的方法相比，该方法可以用更少的计算时间提取更准确的平面。
This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera–SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods.