In this paper we introduce an adaptive cost function for pointcloud registration. The algorithm automatically estimates the sensor noise, which is important for generalization across different sensors and environments. Through experiments on real and synthetic data, we show significant improvements in accuracy and robustness over state-of-the-art solutions.
Università degli Studi di Milano - Bicocca（米兰比可卡大学）
Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state of the art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, to avoid an excessive number of iterations and, therefore, wasting computational time. With this work, we compare different termination criterion on several datasets and prove that the chosen one produce very good results that are comparabl…
We propose PHASER, a correspondence-free global registration of sensor-centric pointclouds that is robust to noise, sparsity, and partial overlaps. Our method can seamlessly handle multimodal information and does not rely on keypoint nor descriptor preprocessing modules. By exploiting properties of Fourier analysis, PHASER operates directly on the sensor's signal, fusing the spectra of multiple channels and computing the 6-DoF transformation based on correlation. Our registration pipeline starts by finding the most likely rotation followed by computing the most likely translation. Both estimat…
The Hong Kong University of Science and Technology（香港科技大学）；Polytechnic University, Hong Kong（香港理工大学）
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global consistency. So in this work, we make the multiview point-cloud registration more tractable from a different perspective in resolving range-based BA. Based on this analysis, we propose an objective function that takes both measurement noises and computational cost into account. For the feature parame…
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Oxford Robotics Institute (ORI), University of Oxford, UK（牛津大学机器人研究所（ORI））；Bristol Robotics Laboratory (BRL), University of the West of England, UK（布里斯托尔机器人实验室（BRL））；Google DeepMind and the University of Cambridge, UK（谷歌DeepMind和剑桥大学）
Adaptive Depth-Map-Guided Bundle Adjustment for Correspondence-Free Multi-View Point Cloud Registration
Robotics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院机器人研究所）；School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）
Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups
Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）；Department of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学）
An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
School of Information Science and Engineering, Wuhan University of Science and Technology（信息科学与工程学院，武汉科技大学）；School of Computer Science and Technology, Wuhan University of Science and Technology（计算机科学与技术学院，武汉科技大学）；School of Information Management and Institute of Big Data and Digital Economy, Hubei University of Economics（信息管理学院和大数据与数字经济研究院，湖北经济学院）
Massachusetts Institute of Technology（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences. We first reformulate the registration problem using a Truncated Least Squares (TLS) cost that is insensitive to a large fraction of spurious correspondences. Then, we provide a general graph-theoretic framework to decouple scale, rotation, and translation estimation, which allows solving in cascade for the three transformations. Despite the fact that each subproblem is still non-convex and combinatorial in nature, we show that (i) TLS sc…
The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Applied Science and Technology Research Institute（香港应用科技研究院）
In this paper, we present a new robotic system to perform defect inspection tasks over free-form specular surfaces. The autonomous procedure is achieved by a six-DOF manipulator, equipped with a line scan camera and a high-intensity lighting system. Our method first uses the object's CAD mesh model to implement a K-means unsupervised learning algorithm that segments the object's surface into areas with similar curvature. Then, the scanning path is computed by using an adaptive algorithm that adjusts the camera's ROI to observe regions with irregular shapes properly. A novel iterative closest p…
