Stack point cloud positioning method based on edge detection and regional growth

A technology of edge detection and region growth, applied in image enhancement, image data processing, instruments, etc., can solve problems such as low segmentation efficiency, easy to be affected by sundries on trucks, difficult to accurately select cluster segmentation distance threshold, etc., to achieve The effect of improving the positioning accuracy of the stack

Pending Publication Date: 2019-09-10
SOUTHEAST UNIV +2
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Problems solved by technology

This method has certain limitations, because the uneven truck compartment is difficult to completely fit the plane, it is easily affected by debris on the truck, and the distance threshold for cluster segmentation is difficult to accurately select, so the segmentation efficiency is low

Method used

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  • Stack point cloud positioning method based on edge detection and regional growth
  • Stack point cloud positioning method based on edge detection and regional growth
  • Stack point cloud positioning method based on edge detection and regional growth

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Embodiment 1

[0068] Taking this example as an example, there are three truck stacks to be identified. After point cloud processing, the stack point cloud blocks that meet the normal & volume criterion are c1,...,c6 respectively. Then calculate the average value of the point cloud for each point cloud block (x i ,y i )(i=1,...,6) as the center coordinates of each pallet. The calculated coordinates of the pallets are shown in Table 1:

[0069] Table 1: Coordinates of the center of the pallet

[0070] Stacking piece 1 2 3 4 5 6 x / m 0.486 0.489 0.298 0.296 0.117 0.118 y / m -1.508 -1.484 0.260 0.284 2.510 2.534

[0071] Since the stacks are arranged according to the Y axis in this example, the y coordinates are sorted (the one with the smaller y is the front), and then the midpoint position is calculated in pairs (X j ,Y j ), j=1,2,3. In addition, in this example, the basic parameters of the stack to be identified are shown in Table 2:

[0072] Ta...

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Abstract

The invention discloses a stack point cloud positioning method based on edge detection and regional growth, which realizes accurate segmentation of a stack point cloud through edge detection, edge point cloud clustering and AABB stack position extraction, realizes effective identification of a stack position through detection and identification of a point cloud block, and improves the stack position positioning precision. The method effectively solves the problem of low segmentation efficiency in the prior art.

Description

technical field [0001] The invention relates to the field of unmanned driving systems in iron and steel plants, in particular to a stack point cloud positioning method based on edge detection and area growth. Background technique [0002] With the development of automation technology, the unmanned management of steel plant storage areas has become an important trend. The 3D laser scanning system is a system that processes the 3D laser point cloud data to locate the stack position on the truck and provides the unmanned vehicle with the target position of the outbound materials in the unmanned vehicle system. The traditional point cloud segmentation and positioning method for stacks needs to carry out plane fitting on the point cloud of the truck compartment, remove the recognized plane of the compartment, and then cluster and segment the point cloud outside the plane, so as to separate the stack from the compartment Come. This method has certain limitations, because the une...

Claims

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Application Information

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IPC IPC(8): G06T7/13G06T7/187G06T7/73
CPCG06T7/13G06T7/187G06T7/73G06T2207/10028
Inventor 牛丹魏双
Owner SOUTHEAST UNIV
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