首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets
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Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets

机译:使用基于体素的4平面全等集对城市场景中的点云进行成对粗注册

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摘要

To ensure complete coverage when measuring a large-scale urban area, pairwise registration between point clouds acquired via terrestrial laser scanning or stereo image matching is usually necessary when there is insufficient georeferencing information from additional GNSS and INS sensors. In this paper, we propose a semi-automatic and target-less method for coarse registration of point clouds using geometric constraints of voxel-based 4-plane congruent sets (V4PCS). The planar patches are firstly extracted from voxelized point clouds. Then, the transformation invariant, 4-plane congruent sets are constructed from extracted planar surfaces in each point cloud. Initial transformation parameters between point clouds are estimated via corresponding congruent sets having the highest registration scores in the RANSAC process. Finally, a closed-form solution is performed to achieve optimized transformation parameters by finding all corresponding planar patches using the initial transformation parameters. Experimental results reveal that our proposed method can be effective for registering point clouds acquired from various scenes. A success rate of better than 80% was achieved, with average rotation errors of about 0.5 degrees and average translation errors less than approximately 0.6 m. In addition, our proposed method is more efficient than other baseline methods when using the same hardware and software configuration conditions.
机译:为了确保在测量大规模市区时完全覆盖,通常在来自其他GNSS和INS传感器的地理参考信息不足时,必须通过地面激光扫描或立体图像匹配获取的点云之间成对配准。在本文中,我们提出了一种使用基于体素的4平面一致集(V4PCS)的几何约束对点云进行粗注册的半自动和无目标方法。首先从体素化点云中提取平面补丁。然后,从每个点云中提取的平面构建变换不变的4平面一致集合。点云之间的初始转换参数是通过在RANSAC过程中具有最高注册分数的对应一致集合来估计的。最后,执行封闭形式的解决方案,以通过使用初始变换参数找到所有对应的平面补丁来获得优化的变换参数。实验结果表明,本文提出的方法可以有效地记录从各种场景获取的点云。成功率超过80%,平均旋转误差约为0.5度,平均平移误差小于0.6 m。此外,当使用相同的硬件和软件配置条件时,我们提出的方法比其他基准方法更有效。

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