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Laser point cloud outdoor scene automatic segmentation method based on machine learning

A laser point cloud and machine learning technology, applied in the field of artificial intelligence recognition, can solve the problems of inability to be fully automated, low recognition efficiency, and low recognition accuracy, and achieve the effects of saving memory, increasing reading speed, and improving recognition accuracy and efficiency

Active Publication Date: 2019-08-16
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The embodiment of the present invention solves the technical problems of low recognition efficiency, low recognition accuracy and incapable of full automation in the field of outdoor point cloud scene reconstruction in the prior art by providing a machine learning-based automatic laser point cloud outdoor scene segmentation method

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  • Laser point cloud outdoor scene automatic segmentation method based on machine learning
  • Laser point cloud outdoor scene automatic segmentation method based on machine learning
  • Laser point cloud outdoor scene automatic segmentation method based on machine learning

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Embodiment Construction

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0019] The present invention simulates the way of thinking of the human brain, because the information processing of the human visual system is hierarchical, and the working process of the brain is a process of continuous iteration and abstract conceptualization, for example, starting from the original signal intake, and then doing preliminary processing, Then abstract, then further abstract, and finally judge and recognize, that is to say, the high-level featu...

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Abstract

The invention relates to a laser point cloud outdoor scene automatic segmentation method based on machine learning. The laser point cloud outdoor scene automatic segmentation method comprises the following steps: extracting each type of voxel data in a database according to an approximately same proportion, storing the voxel data in a training set and a verification set, and compressing all data;importing the compressed training data set and the verification set into a three-dimensional convolutional neural network for operation, so that feature vectors of various ground objects are extracted; feeding back and adjusting the weight value of the deep neural network through a cost function between the feature vector and the actual value of each type of ground object, carrying out repeated iteration until the cost function is smaller than a set threshold value, and then obtaining a model storing the optimal weight value; optimizing the spatial information and the depth information by using the neighborhood algorithm, so that the removed voxel points can be classified, and the previous coarse classification result can be optimized, and fine classification can be realized, and the accuracy and efficiency of scene segmentation can be greatly improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence recognition, in particular to a method for automatically segmenting outdoor scenes of laser point clouds based on machine learning. Background technique [0002] Lidar is a non-contact and active technology to quickly acquire three-dimensional dense point clouds on the surface of objects. It can directly acquire massive and irregularly distributed three-dimensional point clouds with information such as three-dimensional coordinates and echo times, and is less affected by weather. At present, it plays an important role in the fields of global change, smart city, resource survey, environmental monitoring, basic surveying and mapping. However, in actual production at present, due to the complexity of terrain changes, the diversity of ground objects, and the uneven density of points, the reconstruction of point cloud object scenes is done manually or semi-automatically, requiring a lot...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06T7/11G06T7/73G06N3/08G06T2207/10028G06T2207/20081G06T2207/20084G06V20/10G06V10/267G06N3/045G06F18/2413Y02D10/00
Inventor 邓建华申睿涵孙一鸣周群芳何子远钱璨王韬王云邓力恺杨远望游长江管庆于永斌张开元
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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