When multiple robots are involved in the process of simultaneous localization and map**
(SLAM), a global map should be constructed by merging the local maps built by individual …

Three-dimensional surface registration transforms multiple three-dimensional data sets into
the same coordinate system so as to align overlap** components of these sets. Recent …

This article presents an efficient and robust estimator called compatibility-guided sampling
consensus (CG-SAC) to achieve accurate 3-D point cloud registration. For correspondence …

Registration of point sets is done by finding a rotation and translation that produces a best fit
between a set of data points and a set of model points. We use robust M-estimation …

Point cloud registration sits at the core of many important and challenging 3D perception
problems including autonomous navigation, SLAM, object/scene recognition, and …

基于激光雷达的同时定位与地图构建方法综述 Page 1 收稿日期:20180913;修回日期:20181105
基金项目:国家自然科学基金资助项目(41601409);北京市自然科学基金资助项目 (8172016);北京 …

This paper presents a novel randomized algorithm for robust point cloud registration without
correspondences. Most existing registration approaches require a set of putative …

This paper casts multi-view registration as a clustering task that can be solved using an
approach proposed based on K-means clustering. For clustering, all centroids are uniformly …

The traditional iterative closest point (ICP) algorithm is accurate and fast for rigid point set
registration but it is unable to handle affine case. This paper instead introduces a novel …