Semantic map construction method based on convolutional neural network and computer storage medium

A convolutional neural network and semantic map technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as low amount of information, and achieve the effect of improving efficiency and accuracy

Pending Publication Date: 2019-12-06
广州高新兴机器人有限公司
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Problems solved by technology

Simple geometric features or low-dimensional naked-eye features contain too little informa

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  • Semantic map construction method based on convolutional neural network and computer storage medium
  • Semantic map construction method based on convolutional neural network and computer storage medium
  • Semantic map construction method based on convolutional neural network and computer storage medium

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Embodiment Construction

[0037] The specific implementation manner of the present invention will be further described in detail below with reference to the drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0038] The method 100 for constructing a semantic map based on a convolutional neural network according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] The method 100 for constructing a semantic map based on a convolutional neural network according to an embodiment of the present invention includes the following steps: S1, receiving a 2D image, passing it into a convolutional neural network model, and outputting neurons of dense pixel-level semantic probability map points; S2 , Use the Bayesian update model to track the classification probability distribution of each surface; S3, use the ElasticFusion method...

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Abstract

The invention provides a semantic map construction method based on a convolutional neural network and a computer storage medium, and the method comprises the following steps: S1, receiving a 2D image,transmitting the 2D image to a convolutional neural network model, and outputting neurons of dense pixel-level semantic probability map points; S2, tracking classification probability distribution ofeach curved surface by adopting a Bayesian updating model; S3, providing data by adopting an ElasticFusion method to carry out relevance prediction, and updating probability distribution; and S4, improving semantic detection through the scale information of the map by utilizing a conditional random field regularization model. According to the semantic map construction method based on the convolutional neural network, semantic segmentation can be carried out based on the convolutional neural network, the semantic map is generated, and the robustness of the semantic map under few weak texturesis enhanced.

Description

technical field [0001] The present invention relates to the field of navigation systems, more specifically, to a navigation system map construction method, and more specifically, to a convolutional neural network-based semantic map construction method and a computer storage medium. Background technique [0002] The prior art discloses a point cloud semantic map construction method based on deep learning and lidar. This technical solution describes a deep learning based on lidar features and a construction method of semantic map, that is, scanning point clouds based on lidar , using K-nearest neighbors or similar methods for unsupervised learning to expand data, and then introduce convolutional neural networks for semantic recognition. After recognition, semantically label the geometric map formed by lidar, and then construct a corresponding map. In addition, a series of algorithms such as orb-slam and svo have proposed methods based on bag-of-words (BoW) or semi-dense featur...

Claims

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Application Information

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IPC IPC(8): G06T17/05G06T7/50G06T3/40G06K9/62G06N3/04G06N3/08
CPCG06T17/05G06T3/4007G06T7/50G06N3/08G06T2200/08G06T2207/20081G06T2207/20084G06N3/045G06F18/24155G06F18/25
Inventor 柏林于泠汰刘彪
Owner 广州高新兴机器人有限公司
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