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3-D Object Recognition by Computer Vision for Autonomous Working Robot

自主工作机器人的计算机视觉 3D 物体识别

基本信息

批准号：
04650312
负责人：
NOMURA Yoshihiko
金额：
$ 1.34万
依托单位：
Nagoya University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-04650312/
关键词：
Object recognition Model based vision Vision Pattern matching Non-linear optimization Pose estimation Potential field 3-D object 非線形最小二乗法位置・姿勢推定

项目摘要

We have studied some unknown parameter estimation techniques, i.e., matching algorithms between a model object and an input one under this grant.1. Matching based on observed 3-D shape dataWe proposed a shape matching algorithm :Step 1 : Create potential fields for both shape data of model object and input one.Step 2 : Match two potential fields using optimization techniques such as the non-linear least squares method and dynamic programming. 3-D pose, i.e., the positoin and orientation of the input object is estimated as a result of matching.Then, we showed its effectiveness by various simulations. The results are summarized as follows.(1)Fundamental dynamic characteristics of estimation are clarified under various conditions using a simple rectangular parallelepiped object.(2)A simple articulatcd model object is transformed into a completely differint shape of input object.2. Matching based on observed 2-D image dataWe proposed an iconic matching algorithm :Step 1 : Create estimated images assuming pose and shape of a model object.Step 2 : Match the estimated and input images using optimization tecuniques such as the non-linear least squares method and dynamic programming. 3-D pose and shape of the input object is estimated as a resuit of matching.Furthermore, we found that the model object can be transformed into the greatly changed input image by considering the degree of coincidence of both estimated and input images.

我们研究了一些未知参数估计技术，即在此条件下模型对象与输入对象之间的匹配算法。基于观测到的三维形状数据的匹配我们提出了一种形状匹配算法：第一步：为模型对象的形状数据和输入的形状数据创建势场；第二步：利用非线性最小二乘法和动态规划等优化技术对两个势场进行匹配。三维姿态，即作为匹配结果的输入对象的位置和方向被估计，然后我们通过各种仿真验证了它的有效性。研究结果如下：(1)以一个简单的长方体物体为例，阐明了不同条件下估计的基本动力学特性。(2)将一个简单的关节模型物体变换为一个完全不同形状的输入物体。基于观察到的二维图像数据的匹配我们提出了一种标志性的匹配算法：第一步：假设模型物体的姿态和形状创建估计图像；第二步：使用非线性最小二乘法和动态规划等优化技术对估计图像和输入图像进行匹配。通过对输入物体的三维姿态和形状的估计作为匹配的结果，通过考虑估计图像和输入图像的重合度，可以将模型物体转换成变化较大的输入图像。