Visual SLAM front-end pose estimation method based on deep learning

Abstract

The invention provides a visual SLAM front-end pose estimation method based on deep learning. The visual SLAM front-end pose estimation method is used for estimating inter-frame pose transformation inreal time. The method comprises the following steps: firstly, carrying out data preprocessing on an original data set, and then constructing a Brox network to carry out dense optical flow extractionon input continuous frame images; carrying out feature extraction on the extracted optical flow graph by two networks, extracting high-dimensional features from one branch by adopting global information, dividing the optical flow graph into four sub-images at the other branch, and respectively carrying out down-sampling to obtain image features; and finally, fusing the features obtained by training the two branches, and carrying out pose estimation on the final cascade full-connection network to obtain a pose between two adjacent frames. According to the method, the problem of real scale estimation in monocular vision is solved, camera motion and proportion information can be extracted by using global information and local information, and the learning ability and intelligent level of therobot are improved.

Description

technical field [0001] The invention relates to the field of visual navigation, in particular to a visual SLAM front-end pose estimation method. After receiving continuous image frame input in an end-to-end manner, the pose transformation between frames is estimated in real time, which can provide a highly robust visual SLAM method based on deep learning for UAVs. Background technique [0002] Simultaneous Localization and Mapping (SLAM) is a technology in which intelligent bodies such as drones carry their sensors to establish a map of the surrounding environment during movement and perform their own positioning according to the established environmental map. When UAVs work in some special environments, they are susceptible to environmental interference and make the GPS signal weak or completely invalid. In order to make up for the lack of GPS-based navigation systems for UAVs, SLAM can also be used in environments where GPS cannot be used normally. As an effective alterna...

AI Technical Summary

Problems solved by technology

Solve the technical problems of poor universality in the existing visual pose estimation realized by the learning method, and the technical problems of poor real-time performance, difficult feature detection and poor robustness in the visual odometer realized by the geometric method

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