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Target pose estimation method based on adaptive Gaussian weight fast point feature histogram

A point feature histogram, pose estimation technology, applied in computing, image enhancement, image analysis and other directions, can solve the problems of large difference in weight coefficients of neighboring points, low accuracy and efficiency, poor robustness, etc., to overcome robustness Poor performance, reduced time required, and improved real-time pose estimation efficiency

Pending Publication Date: 2021-12-10
NOBLEELEVATOR INTELLIGENT EQUIP CO LTD +1
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Problems solved by technology

[0007] The present invention provides a target pose estimation method based on the adaptive Gaussian weight fast point feature histogram, which overcomes the low accuracy and efficiency caused by the fixed neighborhood radius used in the existing pose estimation technology and the large gap between the weight coefficients of the neighborhood points. , the problem of poor robustness

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  • Target pose estimation method based on adaptive Gaussian weight fast point feature histogram
  • Target pose estimation method based on adaptive Gaussian weight fast point feature histogram
  • Target pose estimation method based on adaptive Gaussian weight fast point feature histogram

Examples

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Embodiment 1

[0062] The target pose estimation method based on the adaptive Gaussian weight fast point feature histogram, specifically includes the following steps:

[0063] Step S1: Read point cloud data: read the pallet template point cloud data and the initial scene point cloud data collected by Kinect V2, the data is in ply format, extract the point cloud three-dimensional coordinate information, there are 78894 points in the template point cloud data, There are 103995 points in the initial scene point cloud data, and the reading results are as follows figure 2 shown;

[0064] Step S2: Data preprocessing: use the pcdownsample function to perform voxel grid preprocessing on the template point cloud data and the initial scene point cloud data, and obtain the preprocessed scene point cloud data;

[0065] Step S3: Obtain the adaptive optimal neighborhood radius: the minimum neighborhood radius r_min=0.006m, the maximum neighborhood radius r_max=0.01 m, the radius interval is 0.001 m, cal...

Embodiment 2

[0085] The target pose estimation method based on the adaptive Gaussian weight fast point feature histogram, specifically includes the following steps:

[0086] Step S1: Read point cloud data: read the template point cloud data "dragonStandRight_0" and the initial scene point cloud data "dragonStandRight_24" in the Kinect dataset. The data is in ply format and only contains 3D coordinate information. There are 41841 template point cloud data points, the initial scene point cloud data has 34836 points;

[0087] Step S2: Data preprocessing: use the pcdownsample function to perform voxel grid preprocessing on the template point cloud data and the initial scene point cloud data, and obtain the preprocessed scene point cloud data;

[0088] Step S3: Obtain the adaptive optimal neighborhood radius: the minimum neighborhood radius r_min=0.006m, the maximum neighborhood radius r_max=0.012m, the radius interval is 0.001m, calculate the feature entropy corresponding to different neighbor...

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Abstract

The invention discloses a target pose estimation method based on an adaptive Gaussian weight fast point feature histogram. The method specifically comprises the following steps: S1, reading point cloud data; S2, preprocessing the data; S3, obtaining an adaptive optimal neighborhood radius; S4, calculating a normal vector; S5, extracting key point; S6, extracting feature; S7, carrying out point cloud coarse registration; S8, carrying out point cloud precise registration; and S9, estimating the pose. Based on neighborhood feature entropy, a self-adaptive optimal neighborhood radius of feature extraction is determined, meanwhile, a distance mean value and a variance between a key point and a neighborhood point are calculated, and a Gaussian weight function of a new feature descriptor is constructed, so that the weight setting of each neighborhood point can more accurately describe the influence of the neighborhood point on the feature of the key point. By adopting the method, the precision and the efficiency of the whole pose estimation process are higher, and the robustness is stronger.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional point cloud processing, in particular, the invention relates to an object pose estimation method based on an adaptive Gaussian weight fast point feature histogram. Background technique [0002] With the development of industrial automation and intelligence, enterprises have higher and higher requirements on the production efficiency and quality of products. The traditional manual-assisted production mode has gradually revealed many shortcomings such as low production efficiency, low product quality compliance rate, large fluctuations in product quality, and high labor costs. The replacement of labor by machines has become the trend and inevitable choice of industrial development. [0003] Vision provides many conveniences for human activities and production, enabling humans to quickly perceive and adapt to the environment. Machine vision is to imitate and replace human eyes through came...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/73G06K9/46G06T7/33
CPCG06T7/73G06T7/33G06T2207/10028
Inventor 邵益平朱宝昌鲁建厦周敏龙佐富兴朱婷婷李亚云周晓静钮超晔
Owner NOBLEELEVATOR INTELLIGENT EQUIP CO LTD
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