Motion Planning System for Bin Picking Using 3-D Point Cloud

In this paper, we propose a motion planning system for bin picking using 3-D point cloud. The situation that the objects are put miscellaneously like the inside in a house is assumed. In the home, the equipment which makes an object stand in line doesn’t exist. Therefore the motion planning system which considered a collision problem becomes important. In this paper, Information on the objects is measured by a laser range finder (LRF). The information is used as 3-D point cloud, and the objects are recognized by model-base. We propose search method of a grasping point for two-fingered robotic hand, and propose search method of a path to approach the grasping point without colliding with other objects.

RANSACs for 3D Rigid Registration:A Comparative Evaluation

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid registration.Random sample consensus(RANSAC)and its variants are popular solutions to this problem.Although there have been a number of RANSAC-fashion estimators,two issues remain unsolved.First,it is unclear which estimator is more appropriate to a particular application.Second,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain ambiguous.This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation.The objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation performance.Experiments have been carried out on four standard datasets with different application scenarios,data modalities,and nuisances.They provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and scales.Based on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

3-D Reconstruction and Visualization of Laser-Scanned Trees by Weighted Locally Optimal Projection and Accurate Modeling Method

This paper presents a method to reconstruct 3-D models of trees from terrestrial laser scan(TLS)point clouds.This method uses the weighted locally optimal projection(WLOP)and the AdTree method to reconstruct detailed 3-D tree models.To improve its representation accuracy,the WLOP algorithm is introduced to consolidate the point cloud.Its reconstruction accuracy is tested using a dataset of ten trees,and the one-sided Hausdorff distances between the input point clouds and the resulting 3-D models are measured.The experimental results show that the optimal projection modeling method has an average one-sided Hausdorff distance(mean)lower by 30.74%and 6.43%compared with AdTree and AdQSM methods,respectively.Furthermore,it has an average one-sided Hausdorff distance(RMS)lower by 29.95%and 12.28%compared with AdTree and AdQSM methods.Results show that the 3-D model generated fits closely to the input point cloud data and ensures a high geometrical accuracy.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

PuzzleNet:Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly

We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and DublinPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.

A study of projections for key point based registration of panoramic terrestrial 3D laser scan

This paper surveys state-of-the-art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images.As modern terrestrial laser scanners digitize their environment in a spherical way,the sphere has to be projected to a two-dimensional image.To this end,we evaluate the equirectangular,the cylindrical,the Mercator,the rectilinear,the Pannini,the stereographic,and the z-axis projection.We show that the Mercator and the Pannini projection outperform the other projection methods.

Skyline-Based Registration of 3D Laser Scans

Acquisition and registration of terrestrial 3D laser scans is a fundamental task in mapping and modeling of cities in three dimensions. To automate this task marker-flee registration methods are required. Based on the existence of skyline features, this paper proposes a novel method. The skyline features are extracted from panoramic 3D scans and encoded as strings enabling the use of string matching for merging the scans. Initial results of the proposed method in the old city center of Bremen are presented.

地面三维激光扫描的点云配准误差研究

针对闭合条件下地面三维激光扫描点云配准产生的闭合差,基于测量平差理论,提出一种闭合差分配方法。首先通过间接平差理论求出各相邻测站间的坐标转换参数及其精度,再对闭合差按与方差成正比分配给各测站。通过多站点云配准实验,验证了该方法能在一定程度上提高点云配准的精度。

自由曲面测量的三维散乱点云无约束配准

结合局部曲面拟合和广义二分优化搜索,提出了用于大尺寸自由曲面形貌测量中多视三维散乱点云自动配准的算法。首先,对点云微小局部区域进行标准最小二乘曲面拟合,根据拟合残差提取给定点云的全部非平坦区;借助图论中"邻接"与"可达"的概念以及非平坦区的空间分布统计特征,进行相邻点云非平坦区的区域聚类计算以及区域匹配,进而自动获得配准位姿初值。然后,计算源点云在目标点云中最靠近点的k邻近,并向k邻近点的局部移动最小二乘拟合曲面做正交投影,以提取对应点。最后,采用广义二分优化搜索进行位姿变换的优化求解。试验结果表明:该方法稳定、可靠,无需人工交互,适用于采样错移情形。在重叠区域内选取150个对应点进行位姿优化时,平均配准缝隙约为0.02mm,可以满足大尺寸自由曲面形貌测量的多视三维散乱点云配准的要求。

逆向工程中散乱点云变尺度配准算法研究

针对传统散乱点云配准算法收敛区间与配准精度之间的矛盾,提出一种变尺度点云配准算法。构造一种基于重合点计数点云配准测度函数;对测度函数的高斯平滑过程进行研究,并对尺度参数对测度函数性能的影响规律进行分析;根据测度函数在大尺度参数下平滑但存在极值偏移,在小尺度参数下全局极值位置精确但存在局部极值的特点,提出一种尺度参数可变的散乱点云配准算法;借鉴模拟退火算法的思想,通过对比选定Lundy退化策略作为算法的尺度衰减策略;采用曲率约束进行控制点筛选并利用快速高斯变换进行测度函数值的计算以提高算法效率;利用合成数据和实测数据进行对比试验,结果基于变尺度策略的散乱点云配准算法具有更加广泛的收敛区间和更高的配准精度。

基于粒子群优化算法的散乱点云数据配准

针对目前散乱点云数据配准算法在精度、速度和优化等方面存在的问题,提出一种基于粒子群优化算法的点云数据配准算法。该算法首先根据数据点之间曲率的相似度函数,采用粒子群优化算法在两组点云数据中搜索可以匹配的点对集合,然后用最近点迭代算法进行二次配准,实现了两组散乱点云数据的精确配准。对比实验表明,该算法配准速度快,效果好。

基于地面三维激光扫描技术的公路路线设计参数提取

将三维激光扫描仪应用于公路测量,通过控制点云采集的参数来优化点云密度分布,采用基于四元数的ICP算法进行配准,提高配准精度。运用局部点离群算法完成点云去噪,得到高质量的公路路面点云,为后续设计参数的提取工作提供了可靠的数据源。最后运用点云边界点识别算法准确提取了公路边界线,并进一步生成了公路中心线及各项路线设计参数。实验表明结果完全满足后续施工设计的需要。

基于单位四元数法的激光点云拼接算法研究

在实际生产应用中经常需要对激光器多次测量所得点云进行点云拼接,为解决激光点云的精确拼接问题,提出了一种基于单位四元数的点云拼接算法,通过计算点云重心点所在曲面的曲率特征点,经过多次迭代寻找最近点,求出点云平移与旋转矩阵的最优解,进而完成点云的自动精确拼接。

平面参数空间的实时三维点云配准方法

针对具有多平面结构的室内环境的三维定位和环境建模,提出了一种在平面参数空间进行配准的实时三维点云配准方法。该方法首先使用一种改进的三维霍夫变换算法快速提取点云中的平面特征,然后使用迭代算法在平面的参数空间中寻找最近平面,最后使用这些平面的对应关系来估计两帧点云之间的位姿变换关系。在一个实验室场景中进行的对比实验表明,该算法能够达到与传统迭代最近点(ICP)算法相似的精度,而且速度大大提升。在仅使用普通笔记本CPU的情况下即可实现实时的点云拼接。

基于激光三维扫描的陶瓷产品三维实体建模

传统建模方法的复杂陶瓷产品建模精度无法得到有效保证,为此提出基于激光三维扫描的陶瓷产品实体建模方法。首先依据激光三维扫描和坐标定位原理,构建一种适用于陶瓷产品的三维扫描模型,提取激光点云数据集合,然后对点云数据进行预处理和特征点拼接,提取陶瓷产品点云集合的几何特征,最后征进行多视角点云全面配准,完成对陶瓷产品三维实体模型的构建。仿真实验结果表明,激光扫描建模方法速度快、精度高、能够将建模误差控制在一个较低的水平。

基于特征的点云精确配准算法

在散乱点云的配准过程中,由于不同次扫描得到的点云模型之间的重叠部分可能较小且点云具有丰富的几何细节,致使传统ICP(Iterative Closest Point)精确配准算法很难得到理想精度。针对这个问题以Chen和Medioni提出的点面距离误差测度函数为基础,结合基于特征的点云配准思想,设计了一种先建立拥有接近的主曲率的匹配点对集合,然后将二次拟合曲面间的平均距离作为误差测度进行迭代优化的精确配准算法。该算法在微小距离精确配准的应用环境下能提供相对于传统ICP算法更好的精度和更高的效率。

结构光三维视觉测量关键技术的研究

结构光作为一种主动式、非接触的三维视觉测量新技术,在逆向工程、质量检测、数字化建模等领域具有无可比拟的优势。为提高结构光视觉测量的精度、自动化程度,在一些关键技术上提出了有效的方法;并设计了基于标志点的数据拼接技术和移动式结构光三维视觉测量系统,扩大了结构光系统的测量范围;最后介绍了测量点云数据后期处理方面的研究成果。

一种新的尖轨点云配准方法及算法加速研究

为满足高速道岔检测的严格要求,解决尖轨检测中三维点云配准定位难的问题,提出利用尖轨轨腰圆孔特征配准的方法。首先在轨腰上制造圆孔特征,再提取圆孔边界作为特征初始匹配,最后以轨腰作为基准精确匹配。针对边界提取算法中运算效率低下的问题,提出基于异构计算语言的边界提取并行算法。通过分析数据和算法特点,改进了点云数据的存储方式并修改了部分算法以提高计算效率。试验表明,提出的方法有效解决了尖轨配准中定位问题并满足检测标准;改进的算法相对于原算法提高了计算效率,并保证了和系统良好的兼容性。

基于散乱点云的三维人体自动测量

为解决Kinect深度相机三维人体扫描重建中人体特征尺寸提取的问题,提出一种基于散乱点云的三维人体自动测量方案。首先,对采集三维点云数据进行预处理,经点云降采样、离群点滤波和表面重建,以及点云坐标转换,进而识别出人体正背面;然后,利用人体几何形状分析法,自动提取人体特征点和特征截面点云;最后,对得到的特征截面点云提取特征边界点,再通过基于凸壳的轮廓线提取算法得到特征边界线,通过三次B样条曲线拟合计算围度、弧长尺寸,利用坐标差值和欧氏距离计算长度尺寸,从而完成对三维人体点云模型的特征尺寸测量。结果表明,该方案受人体体型因素影响较小,所提出的尺寸信息的自动提取方案有效,并符合相关标准中测量精度的要求。

基于kinect传感器的物体三维重建

随着计算机视觉的发展及深度传感器的出现,基于kinect传感器的物体三维重建技术得到了广泛的研究。针对现有的基于kinect传感器的物体重建算法需要将待重建物体置于转台并随转台转动的局限性,提出了一种通用的基于kinect传感器获取的物体彩色和深度图像完成物体三维重建的方法,对深度图像进行预处理以平滑噪声并获取三维点云,提取彩色图像匹配点并根据彩色图像特征点与三维点云之间的关系得到三维特征点,并通过SVD方法完成点云初始配准,然后利用改进的ICP算法完成点云的精确配准,得到物体表面的三维点云,重建出物体的三维模型。实验结果表明,该方法能有效去除深度图像的噪声,并对各个视角下的点云数据进行精确配准,完成物体的三维重建并得到较好的重建效果。