Correspondence-based rotation search and point cloud registration are two fundamental problems in robotics and computer vision. However, the presence of outliers, sometimes even occupying the great majority of the putative correspondences, can make many existing algorithms either fail or have very high computational cost. In this paper, we present RANSIC (RANdom Sampling with Invariant Compatibility), a fast and highly robust method applicable to both problems based on a new paradigm combining random sampling with invariance and compatibility. Generally, RANSIC starts with randomly selecting small subsets from the correspondence set, then seeks potential inliers as graph vertices from the random subsets through the compatibility tests of invariants established in each problem, and eventually returns the eligible inliers when there exists at least one K-degree vertex (K is automatically updated depending on the problem) and the residual errors satisfy a certain termination condition at the same time. In multiple synthetic and real experiments, we demonstrate that RANSIC is fast for use, robust against over 95% outliers, and also able to recall approximately 100% inliers, outperforming other state-of-the-art solvers for both the rotation search and the point cloud registration problems.
IRON: Invariant-based Highly Robust Point Cloud Registration
East China University of Science and Technology（华东理工大学）
在本文中，我们提出了IRON（基于不变量的全局鲁棒估计和优化），一个非最小的和高度鲁棒的解决方案，点云配准与大量的离群点之间的对应关系。为了实现这一点，我们将配准问题解耦为尺度、旋转和平移的估计。我们的第一个贡献是提出了RANSIC（RANdom Samples with Invariant Compatibility），它利用不变相容性从随机样本中寻找内点，同时鲁棒地估计两组点云之间的尺度。一旦规模估计，我们的第二个贡献是放松到一个凸半定规划（SDP）的非凸全局注册问题，在可证明的方式使用平方和（SOS）松弛，并表明放松是紧的。对于鲁棒估计，我们进一步提出了RT-GNC（粗修剪和分级非凸性），一个全局离群拒绝启发式具有更好的鲁棒性和时间效率比传统的GNC，作为我们的第三个贡献。有了这些贡献，我们可以呈现我们的配准算法IRON。通过在真实数据集上的实验，我们证明了IRON是高效的，高度准确的，并且对多达99%的离群值具有鲁棒性，无论规模是已知的还是未知的，都优于现有的最先进的算法。
PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registration
Colorado School of Mines（科罗拉多矿业大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；U.S. Army Combat Capabilities Development Command, Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）
TurboReg: TurboClique for Robust and Efficient Point Cloud Registration
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；Department of Computer and Systems Engineering, University of Zaragoza（阿拉维扎大学计算机与系统工程系）；College of Computer Science, Beijing University of Technology（北京理工大学计算机学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
GMOR: A Lightweight Robust Point Cloud Registration Framework via Geometric Maximum Overlapping
Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology（北京混合现实与先进显示工程研究中心，光学与 photonics 学院，北京理工大学）；Zhengzhou Research Institute, Beijing Institute of Technology（郑州研究院，北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Medical Technology, Beijing Institute of Technology（医学技术学院，北京理工大学）
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
University of Chinese Academy of Sciences（中国科学院大学）；AIR, Tsinghua University（清华大学AIR）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）
