Visual mileometer method based on convolutional neural network

Abstract

The invention relates a visual mileometer method based on the convolutional neural network. The method comprises steps S1, original environment data is collected by a camera carried by a mobile robot,and a feature point detector A based on the convolutional neural network is trained; S2, the motion of the to-be-estimated mileage is performed by the mobile robot, and to-be-estimated original datais collected by the carried camera; S3, data sampling, trimming and pre-processing operations of the to-be-estimated data collected by the camera are performed to obtain to-be-processed data; S4, thefeature point detector A is utilized to filter to-be-detected data to obtain the feature point information; and S5, the feature point information is utilized in combination with the polar constraint method to solve a motion estimation matrix of a moving subject, and mileage coordinates are further reckoned. The method is advantaged in that changes between frame environments before and after filtering can be associated to obtain the more stable feature points, matching accuracy is enhanced, and thereby an estimation error of the visual mileometer is reduced.

Description

technical field [0001] The invention relates to the technical field of navigation and positioning, in particular to a visual odometry method based on a convolutional neural network. Background technique [0002] Visual odometry technology is mainly used in robot positioning and navigation. In recent years, with the upsurge of research in the field of autonomous driving, visual odometry technology has been widely researched and applied. The method of visual odometry overcomes the problem that traditional GPS odometers cannot provide stable and accurate positioning and navigation in indoor environments or partially outdoor environments, especially in environments where the GPS signal is interfered or even blocked by buildings. [0003] Most of the traditional visual odometry methods are based on the traditional feature point detection method, and these methods often only focus on the feature points of a local area in a single picture, and do not pay attention to the changes a...

AI Technical Summary

Problems solved by technology

The method of visual odometry overcomes the problem that traditional GPS odometers cannot provide stable and accurate positioning and navigation in indoor environments or partially outdoor environments, especially in environments where the GPS signal is interfered or even blocked.

[0003] Most of the traditional visual odometry methods are based on the traditional feature point detection method, and these methods often only focus on the feature points of a local area in a single picture, and do not pay attention to the changes and connections of the environmental background between consecutive frames. Therefore, It is often affected by it, so there will be many mismatched point pairs or insufficient matched point pairs, which will eventually lead to unavoidable odometer estimation errors

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