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Object grabbing region positioning method based on point cloud registering

A technology of point cloud registration and area positioning, which is applied in image data processing, instruments, character and pattern recognition, etc., can solve the problem of few practical service robots and achieve excellent performance

Active Publication Date: 2017-03-22
北京睿思奥图智能科技有限公司
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Problems solved by technology

At present, some typical object grasping algorithms are generally used in industrial robots, but few in practical service robots.

Method used

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  • Object grabbing region positioning method based on point cloud registering
  • Object grabbing region positioning method based on point cloud registering

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific preferred embodiments.

[0035] Such as figure 1 As shown, a method for locating an object grabbing area based on point cloud registration includes the following steps.

[0036] Step 1, the 3D model point cloud library is established.

[0037] 1. First, use a 3D scanner to scan point clouds of various items. The various items here are mainly daily necessities, preferably including cups, teapots, biscuit boxes, cans and Coke bottles. You can choose two or more of them according to your needs, but it can also be other daily necessities.

[0038] 2. Then store the point cloud models Model of various items formed by point cloud scanning in the 3D model point cloud library.

[0039] 3. At the same time, mark the grabbing area for each point cloud model Model in the 3D model point cloud library. The labeling of the grabbing area here can be automatic l...

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Abstract

The invention discloses an object grabbing region positioning method based on point cloud registering. The positioning method comprises a preprocessing part and an object grabbing positioning part. The preprocessing part comprises establishment of a three dimensional model point cloud library and generation of an SVM classifier. The object grabbing positioning part mainly comprises original image data acquisition of a to-be-grabbed object, image segmentation based on region increasing, region screening of the to-be-grabbed object, generation of Point Cloud data, point cloud registering, and transformation of a grabbing region marked on a cloud point Model by use of transfer matrix Transform and reflection of the changed grabbing region to a 2D image through a built-in camera of a real-sensing 3D camera, thereby finishing grabbing of the object. During the cloud point registering, coarse matching is performed by use of the RANSAC algorithm, and fine tuning is performed by use of the ICP algorithm. According to the invention, desktop-level objects can be automatically detected and positioned; the positioning precision is high; and results are output within seconds without usage of a GPU operation unit.

Description

technical field [0001] The invention relates to the field of drive for positioning and grasping desktop objects, in particular to a method for locating an object grasping area based on point cloud registration. Background technique [0002] In the process of studying robot behavior cognition, it is a novel idea to drive the manipulator to grasp objects from machine vision. It is a new and widely used topic for robots to fully automatically rely on object positioning, recognition and grasping of objects. A more vivid example is that the emerging elderly care industry requires service robots to be able to complete simple object grasping tasks, such as serving a glass of water for people to eat. At present, some typical object grasping algorithms are generally used in industrial robots, but few in practical service robots. Among them, the main algorithm using the visual method is still based on the traditional machine learning method to train the specific marked area. [000...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T2207/10024G06T2207/10012G06F18/2411
Inventor 路通周逸徉蔡敏
Owner 北京睿思奥图智能科技有限公司
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