The Iterative Closest Point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.
In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate.…
We propose a method for speeding up a 3D point cloud registration through a cascading feature extraction. The current approach with the highest accuracy is realized by iteratively executing feature extraction and registration using deep features. However, iterative feature extraction takes time. Our proposed method significantly reduces the computational cost using cascading shallow layers. Our idea is to omit redundant computations that do not always contribute to the final accuracy. The proposed approach is approximately three times faster than the existing methods without a loss of accuracy…
An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry
Université de Lorraine（洛林大学）；CNRS, CRAN, UMR7039（法国国家科学研究中心，CRAN，UMR7039）；Université Laval（拉瓦尔大学）；FORAC Research Consortium, Université Laval（FORAC研究联盟，拉瓦尔大学）；University of Toronto（多伦多大学）
Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机器人与机制实验室（RoMeLa）、机械与航空航天工程系、加州大学洛杉矶分校）
Course estimation is a key component for the development of autonomous navigation systems for robots. While state-of-the-art methods widely use visual-based algorithms, it is worth noting that they all fail to deal with the complexity of the real world by being computationally greedy and sometimes too slow. They often require obstacles to be highly textured to improve the overall performance, particularly when the obstacle is located within the focus of expansion (FOE) where the optic flow (OF) is almost null. This study proposes the FAst ITerative Half-plane (FAITH) method to determine the co…
German Research Center for Artificial Intelligence – DFKI（德国人工智能研究中心）
Despite the remarkable progress of deep learning in stereo matching, there exists a gap in accuracy between real-time models and slower state-of-the-art models which are suitable for practical applications. This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap by allowing it to adopt any stereo matching network to make it fast, more efficient and scalable while keeping comparable accuracy. To reduce the computational cost of matching, we use multi-scale warped features to estimate disparity residuals and push the disparity search range in th…
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
Fraunhofer Institute for Telecommunications–Heinrich Hertz Institute（弗劳恩霍夫电信研究所-海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Experimental and Clinical Research Center（实验与临床研究中心）；Charité Medical Faculty（夏里特医学院）；Max-Delbrueck Center for Molecular Medicine（马克斯·德尔布吕克分子医学中心）；HELIOS Klinikum Berlin Buch（柏林布赫赫利俄斯医院）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院（PTB））；King’s College London（伦敦国王学院）
