IRON: Invariant-based Highly Robust Point Cloud Registration
IRON: Invariant-based Highly Robust Point Cloud Registration
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IRON：基于不变性的高度鲁棒点云配准
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Lei Sun
中科院分区：
文献类型：
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作者：
Lei Sun
在本文中，我们提出了 IRON（基于不变的全局鲁棒估计和优化），这是一种非最小且高度鲁棒的点云配准解决方案，在对应关系中存在大量异常值。为了实现这一点，我们将配准问题分别解耦为缩放、旋转和平移的估计。我们的第一个贡献是提出 RANSIC（具有不变兼容性的随机样本），它利用不变兼容性来寻找随机样本中的内点，同时稳健地估计两组点云之间的尺度。一旦估计了规模，我们的第二个贡献是使用平方和（SOS）松弛以可证明的方式将非凸全局配准问题松弛为凸半定规划（SDP），并表明松弛是严格的。为了稳健估计，我们进一步提出 RT-GNC（粗略修剪和分级非凸性），这是一种比传统 GNC 具有更好鲁棒性和时间效率的全局异常值拒绝启发式算法，作为我们的第三个贡献。有了这些贡献，我们就可以渲染我们的配准算法 IRON。通过对真实数据集的实验，我们表明，无论规模是已知还是未知，IRON 都是高效、高度准确且稳健的，能够应对多达 99% 的异常值，其性能优于现有的最先进算法。
In this paper, we present IRON (Invariant-based global Robust estimation and OptimizatioN), a non-minimal and highly robust solution for point cloud registration with a great number of outliers among the correspondences. To realize this, we decouple the registration problem into the estimation of scale, rotation and translation, respectively. Our first contribution is to propose RANSIC (RANdom Samples with Invariant Compatibility), which employs the invariant compatibility to seek inliers among random samples and robustly estimates the scale between two sets of point clouds in the meantime. Once the scale is estimated, our second contribution is to relax the non-convex global registration problem into a convex Semi-Definite Program (SDP) in a certifiable way using Sum-of-Squares (SOS) Relaxation and show that the relaxation is tight. For robust estimation, we further propose RT-GNC (Rough Trimming and Graduated Non-Convexity), a global outlier rejection heuristic having better robustness and time-efficiency than traditional GNC, as our third contribution. With these contributions, we can render our registration algorithm, IRON. Through experiments over real datasets, we show that IRON is efficient, highly accurate and robust against as many as 99% outliers whether the scale is known or unknown, outperforming the existing state-of-the-art algorithms.