一种融合多帧ICP和图优化的算法研究

当前基于迭代最近点拼接的同时定位与建图算法,存在误差积累、无法满足大范围定位精度的缺陷。为此,提出一种融合多帧迭代最近点和图优化的算法。在时域上处理点云拼接问题,将单帧迭代最近点算法推广到多帧进行最近点迭代,提取同一地点在不同时刻的数据特征,形成多个封闭循环,再运用基于最小二乘的图优化方法对点云拼接后的全网数据进行全局优化,消除累计误差,提升整体的定位精度。采用鲁巷和密歇根的数据进行测试,结果表明,该方法在一定程度上减少了匹配误差,平均误差为1.0m,最小误差为0.2m,可以满足大范围同步定位与建图的精度需求。     

基于改进迭代最近点算法的接骨板贴合性快捷计算方法

为了快速计算出接骨板在断骨表面的最佳贴合位置,以减少手术中接骨板反复调整的次数,提出了一种基于改进迭代最近点（ICP）算法的接骨板贴合性快捷计算方法。首先,由医生指导在断骨表面选取贴合区域,并利用接骨板表面点的法向量之间的夹角提取接骨板的内曲面点云;然后,在对两组点云模型进行平滑处理并采用格点采样的方式来简化点云模型后,利用点云之间的特征关系进行初始配准;最后,对接骨板内曲面点云模型进行边界及内部特征关键点提取,并采用K-维树（KD-Tree）搜索邻近点,对接骨板的特征关键点和断骨表面选取区域执行ICP精确配准。以胫骨为例进行实验,实验结果表明,所提算法相较于近年所提配准算法在保持较高配准度的同时提高了配准效率,能够实现胫骨不同受损类型与接骨板之间的快速配准,并且对其他受损骨骼具有通用性。     

基于RGB-D数据的目标分割与实时重建方法

应用RGB-D传感器进行三维重建具有速度和成本的优势,并可获取纹理信息,但扫描数据易受干扰。针对有干扰的外部环境,提出一种基于RGB-D数据的目标分割与重建方法。首先为改善输入图像质量,采用多帧叠加的中值滤波对信息缺失的位置进行预处理。接着,利用静态减除和动态3D Mean-shift相结合的方法分割目标,实现对目标物体的实时定位。最后使用一种优化的点到面ICP方法进行配准,并在此基础上利用随机抽样算法对点云的配准进行加速。采用该方法实现的原型系统能够支持含有背景和一定干扰下的小物体配准,且能有效提高三维重建的速度和自动化程度。     

基于径向曲线与支持向量回归的颅骨修复方法

颅骨修复技术是对有缺损的颅骨补全对应的缺损部分,进而实现颅骨形状的完整性。针对高维颅骨数据,采用径向曲线来表示颅骨几何特征,结合最小二乘支持向量回归的方法构建颅骨修复模型。提取完整的三维颅骨模型的径向曲线,将其分为已有径向曲线和缺失径向曲线两部分作为训练样本,采用最小二乘支持向量回归统计模型复原出待修复颅骨的缺失径向曲线,进而合并生成待修复颅骨的完整径向曲线,通过迭代最近点算法将合并的颅骨径向曲线与颅骨统计模型进行匹配生成完整的三维颅骨模型。实验结果表明,该方法的平均误差达到6.834×10^-3,比主成分分析方法降低2.90倍,具有更好的修复效果。     

Multi-sensor calibration through iterative registration and fusion

In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods.     

ICP上载作品的著作权思考

本文从ICP的定义与法律特征入手，较深入的探讨了ICP上载作品涉及的著作权问题，最后对ICP上载作品提出了立法思路建议。     

均匀分割主曲线的三维形状描述

将无序的、分布不均匀的三维网格模型形状分布问题,转换为有序的、均匀的一维流形问题,进而实现三维模型的形状检索．本文将三维网格模型,投影为三维的光顺主曲线;通过对主曲线沿弧长方向的均匀分割采样,提取主曲线的空间位置和姿态等形状特征;最后通过形状描述向量的比较实现了对应的三维模型检索．实验证明该形状描述方法对几何变换是稳定的,降低了多分辨率、局部形变、噪声等因素影响对三维网格模型形状描述的干扰．     

Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes

The computation of a rigid body transformation which optimally aligns a set of measurement points with a surface and related registration problems are studied from the viewpoint of geometry and optimization. We provide a convergence analysis for widely used registration algorithms such as ICP, using either closest points (Besl and McKay, 1992) or tangent planes at closest points (Chen and Medioni, 1991) and for a recently developed approach based on quadratic approximants of the squared distance function (Pottmann et al., 2004). ICP based on closest points exhibits local linear convergence only. Its counterpart which minimizes squared distances to the tangent planes at closest points is a Gauss–Newton iteration; it achieves local quadratic convergence for a zero residual problem and—if enhanced by regularization and step size control—comes close to quadratic convergence in many realistic scenarios. Quadratically convergent algorithms are based on the approach in (Pottmann et al., 2004). The theoretical results are supported by a number of experiments; there, we also compare the algorithms with respect to global convergence behavior, stability and running time.     

一种域分布式合作Web缓存系统

在分析Internet缓存协议（ICP）和基于集中管理的协作式Web缓存系统（CMCS）的基础上，提出了一种域分布合作Web缓存系统模型（DDCCS），该模型克服了ICP的缓存内容冗余问题和CMCS集中管理的不健壮问题，并能充分考虑各代理缓存器的处理能力和缓存能力，采用区间散化方法和加权区间片分配算法，使得各代理的负担更加均匀合理，在性能上较ICP和CMCS有较大的改进。     

Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology

In multisensor coordinate metrology scenarios involving the fusion of homogenous data, specifically 3D point clouds like those originated by CMMs and structured light scanners, the problem of registration, i.e. the proper localization of the clouds in the same coordinate system, is of central importance. For fine registration, known variants of the Iterative Closest Point (ICP) algorithm are commonly adopted; however, no attempt seems to be done to tweak such algorithms to better suit the distinctive multisensor nature of the data. This work investigates an original approach that targets issues which are specific to multisensor coordinate metrology scenarios, such as coexistence of point sets with different densities, different spatial arrangements (e.g. sparse CMM points vs. gridded sets from light scanners), and different noise levels associated to the point sets depending on the metrological performances of the sensors involved. The proposed approach is based on combining known ICP variants with novel point set augmentation techniques, where new points are added to existing sets with the purpose of improving registration performance and robustness to measurement error. In particular, augmentation techniques based on advanced fitting solutions promote a paradigm shift for registration, which is not seen as a geometric problem consisting in moving point sets as close as possible to each other, but as a problem where it is not the original points, but the underlying geometries that must be brought together. In this work, promising combinations of ICP and point augmentation techniques are investigated through the application to virtual scenarios involving synthetic geometries and simulated measurements. Guidelines for approaching registration problems in industrial scenarios involving multisensor data fusion are also provided.