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Point cloud data division method and device and obstacle detection method and device

A technology of obstacle detection and point cloud data, applied in the computer field

Active Publication Date: 2020-05-29
ZOOMLION HEAVY IND CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

LiDAR is robust to interference factors such as lighting and color, but is vulnerable to snow, fog lights and bad weather

Method used

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  • Point cloud data division method and device and obstacle detection method and device
  • Point cloud data division method and device and obstacle detection method and device
  • Point cloud data division method and device and obstacle detection method and device

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Embodiment Construction

[0029] The specific implementation manners of the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation manners described here are only used to illustrate and explain the embodiments of the present invention, and are not intended to limit the embodiments of the present invention.

[0030] figure 1 A schematic flowchart of an obstacle detection method according to an embodiment of the present invention is shown. Such as figure 1 As shown, the embodiment of the present invention provides an obstacle detection method, which can be applied to obstacle detection of various types of construction machinery. The method may include: step S110, acquiring a depth image of the current environment; step S120, dividing the point cloud data set corresponding to the depth image to obtain multiple sub-point cloud data sets; step S130, for each sub-point cloud data set The...

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Abstract

The embodiment of the invention provides a point cloud data division method and device and an obstacle detection method and device, and belongs to the technical field of computers. The point cloud data partitioning method comprises the steps of performing three-dimensional reconstruction on a depth image of a current environment to generate a point cloud data set, wherein one piece of point clouddata in the point cloud data set corresponds to one pixel point in the depth image; obtaining histogram distribution of the depth image based on pixel point depth values; establishing a plurality of depth intervals according to the number of pixel points corresponding to each histogram dimension in the histogram distribution; and dividing the point cloud data set into a plurality of sub-point cloud data sets corresponding to a plurality of depth spaces according to the point cloud data corresponding to the pixel points included in each of the plurality of depth intervals. According to the method, a point cloud data truncation phenomenon can be avoided, and the adaptability to a scene is higher.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a point cloud data division method and device, and an obstacle detection method and device. Background technique [0002] As the primary link of unmanned operation of equipment, environmental perception mainly includes two solutions: lidar and binocular vision. LiDAR is robust to interference factors such as lighting and color, but is vulnerable to snow, fog lights and bad weather. Compared with lidar, binocular vision can not only obtain three-dimensional information of the environment, but also output color images with complex features, and the cost is lower. Therefore, binocular vision is currently widely used in unmanned driving, virtual reality and other fields, especially in obstacle detection in these fields. Contents of the invention [0003] The purpose of the embodiment of the present invention is to provide a point cloud data processing method and device,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T17/00
CPCG06T7/0002G06T17/00G06T2207/10028
Inventor 武鸿范卿曾杨谭智仁付玲雷美玲
Owner ZOOMLION HEAVY IND CO LTD
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