Three-dimensional object detection method based on Hash description and iteration nearest point

A technology that iterates the closest point and three-dimensional objects. It is applied in image data processing, instruments, calculations, etc., and can solve the problems of slow recognition speed and low positioning accuracy.

Inactive Publication Date: 2019-05-21
SICHUAN CHANGHONG ELECTRIC CO LTD
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0003] The pose recognition method for 3D objects in the existing technology needs to extract a large amount of feature information and feature matching from 3D data, the recognition speed is slow, the positioning accuracy is very low, and it is impossible to realize fast and accurate 3D object pose recognition

Method used

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  • Three-dimensional object detection method based on Hash description and iteration nearest point
  • Three-dimensional object detection method based on Hash description and iteration nearest point
  • Three-dimensional object detection method based on Hash description and iteration nearest point

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Embodiment

[0091] A three-dimensional object detection method based on hash description and iterative closest point, including a depth camera and a PC, further comprising the following steps:

[0092] S1. The depth camera collects the scene data of the object to be detected;

[0093] S2. The depth camera emits infrared rays to the scene, and the infrared receiver in the depth camera will receive the near-infrared reflection of the scene to generate three-dimensional point data of the scene, that is, the XYZ information of each reflection point in the scene relative to the coordinate system of the depth camera;

[0094] S3. The depth camera transmits the collected scene data to the PC for storage, and the storage format is PLY format;

[0095] S4. Using the scene data in PLY format acquired in S3 to extract the target model M of the detected object;

[0096] S5, the PC calculates the covariance matrix of each point of the three-dimensional model point cloud, obtains the eigenvector of th...

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Abstract

The invention discloses a three-dimensional object detection method based on Hash description and an iteration nearest point. Posture data of a target object is detected in three-dimensional information; According to the method, more abundant operation tasks and more replicated operation operations can be completed by operation tools such as a robot, structured production is converted into unstructured production, three-bit data is uniquely explained by adopting Hash description, matching of invalid data is reduced, and the feature matching speed is increased. And a point-to-tangent plane iteration nearest point method is adopted, so that the matching is more reliable, and the precision is higher.

Description

technical field [0001] The invention relates to the field of three-dimensional object recognition in machine vision, in particular to a three-dimensional object detection method based on hash description and iterative closest point. Background technique [0002] The method of perceiving the operation scene through machine vision technology has been widely used in express sorting, target distance measurement, automatic assembly and other fields. Part of the automated production operation needs. With the gradual advancement of the national industry 4.0, the requirements for the automation of the manufacturing industry are getting higher and higher, and the recognition and positioning technology based on two-dimensional images is stretched. With the maturity of 3D vision sensor technology and the decline in price, more and more application scenarios are trying to use 3D vision sensors to obtain 3D information of the operation scene, and to detect the position and attitude of t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/73
Inventor 杨厚易
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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