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    • 11. 发明申请
    • SYSTEM AND METHOD OF DETERMINING OBJECT POSE
    • 系统和方法确定对象的位置
    • WO2008036354A1
    • 2008-03-27
    • PCT/US2007/020365
    • 2007-09-19
    • BRAINTECH CANADA, INC.ABRAMONTE, FrankBOCA, Remus, F.PESCARU, Simona LilianaBEIS, Jeffrey ScottHABIBI, Babak
    • BOCA, Remus, F.PESCARU, Simona LilianaBEIS, Jeffrey ScottHABIBI, Babak
    • G01B11/25G01B11/00G01S17/06
    • G01B11/25G01S17/875
    • Briefly described, one embodiment determines pose of an object of interest at a run time by capturing a first image of a first structured light pattern projected onto a first local surface of the object of interest; determining a first run-time data set from the captured first image, wherein the first run-time data set corresponds to information determined from the first structured light pattern projected onto the first local surface; comparing the determined first run-time data set and a corresponding first reference data set, the first reference data set corresponding to an ideal pose of the first local surface on an ideally posed reference object; and determining at least one first degree of constraint that defines a first partial pose of the first local surface, the at least one first degree of constraint based upon the comparison of the first run-time data set with the corresponding first reference data set.
    • 简要描述,一个实施例通过捕获投射到感兴趣对象的第一局部表面上的第一结构化光图案的第一图像,在运行时确定感兴趣对象的姿态; 从所捕获的第一图像确定第一运行时数据集,其中所述第一运行时数据集对应于从投射到所述第一局部表面上的所述第一结构光图案确定的信息; 将所确定的第一运行时数据集和对应的第一参考数据集进行比较,所述第一参考数据集对应于理想姿势的参考对象上的第一局部表面的理想姿势; 以及基于所述第一运行时数据集与所述对应的第一参考数据集的比较来确定定义所述第一局部表面的第一部分姿态的所述至少一个第一约束度,所述至少一个第一约束度。
    • 12. 发明申请
    • METHOD AND APPARATUS FOR MACHINE-VISION
    • 用于机器视觉的方法和设备
    • WO2006019970A3
    • 2006-09-08
    • PCT/US2005025083
    • 2005-07-14
    • BRAINTECH CANADA INCABRAMONTE FRANKBOCA REMUSPESCARU SIMONAHABIBI BABAKSAMETI MOHAMMAD
    • BOCA REMUSPESCARU SIMONAHABIBI BABAKSAMETI MOHAMMAD
    • G06T7/00
    • G06K9/32G06T7/593G06T7/74G06T7/75G06T7/80
    • A system and method facilitate machine-vision, for example three-dimensional pose estimation for target objects, using one or more images sensors to acquire images of the target object at one or more positions, and to identify features of the target object in the resulting images. A set of equations is set up using sparse model information such as the constancy of distances, angles and enclosed volumes between features. The set of equations may be solved to estimate a 3D pose. The number of positions may be determined based on the number of image sensors, number of features identified, and/or number of known physical relationships between less than all features. A knowledge of physical relationships between various image sensors and/or between features and image sensors may be employed. The pose may be used to transform a robot path, to align the path with a current position of the target object.
    • 系统和方法利用一个或多个图像传感器来获取机器视觉,例如用于目标物体的三维姿态估计,以获取目标物体在一个或多个位置处的图像,并识别目标物体在所得结果中的特征 图片。 使用稀疏模型信息建立一组等式,例如距离,角度和特征之间的封闭体积的恒定性。 该组方程可以被求解以估计3D姿态。 可以基于图像传感器的数量,所标识的特征的数量和/或少于所有特征之间的已知物理关系的数量来确定位置的数量。 可以采用各种图像传感器之间和/或特征与图像传感器之间的物理关系的知识。 该姿态可用于变换机器人路径,以将路径与目标对象的当前位置对齐。
    • 13. 发明申请
    • METHOD AND APPARATUS FOR MACHINE-VISION
    • 机器视觉方法与装置
    • WO2006019970A2
    • 2006-02-23
    • PCT/US2005/025083
    • 2005-07-14
    • BRAINTECH CANADA, INC.ABRAMONTE, FrankBOCA, RemusPESCARU, SimonaHABIBI, BabakSAMETI, Mohammad
    • BOCA, RemusPESCARU, SimonaHABIBI, BabakSAMETI, Mohammad
    • G06T7/00
    • G06K9/32G06T7/593G06T7/74G06T7/75G06T7/80
    • A system and method facilitate machine-vision, for example three-dimensional pose estimation for target objects, using one or more images sensors to acquire images of the target object at one or more positions, and to identify features of the target object in the resulting images. A set of equations is set up using sparse model information such as the constancy of distances, angles and enclosed volumes between features. The set of equations may be solved to estimate a 3D pose. The number of positions may be determined based on the number of image sensors, number of features identified, and/or number of known physical relationships between less than all features. A knowledge of physical relationships between various image sensors and/or between features and image sensors may be employed. The pose may be used to transform a robot path, to align the path with a current position of the target object.
    • 系统和方法便于机器视觉,例如使用一个或多个图像传感器在一个或多个位置处获取目标对象的图像的目标对象的三维姿态估计,以及识别所产生的目标对象的特征 图片。 使用诸如距离的恒定性,特征之间的角度和封闭体积的稀疏模型信息来建立一组方程。 可以求解该方程组以估计3D姿态。 可以基于图像传感器的数量,识别的特征数量和/或小于所有特征之间的已知物理关系的数量来确定位置数量。 可以采用关于各种图像传感器之间和/或特征和图像传感器之间的物理关系的知识。 姿势可以用于变换机器人路径,以将路径与目标对象的当前位置对齐。