A fast monocular vision odometer navigation and positioning method combining a feature point method and a direct method

Abstract

The invention discloses a fast monocular vision odometer navigation and positioning method fusing a feature point method and a direct method, which comprises the following steps: S1, starting the vision odometer and obtaining a first frame image I1, converting the image I1 into a gray scale image, extracting ORB feature points, and constructing an initialization key frame; 2, judging whether thatinitialization has been carry out; If it has been initialized, it goes to step S6, otherwise, it goes to step S3; 3, defining a reference frame and a current frame, extracting ORB feature and matchingfeatures; 4, simultaneously calculating a homography matrix H and a base matrix F by a parallel thread, calculating a judgment model score RH, if RH is great than a threshold value, selecting a homography matrix H, otherwise selecting a base matrix F, and estimating a camera motion according to that selected model; 5, obtaining that pose of the camera and the initial 3D point; 6, judging whetherthat feature point have been extracted, if the feature points have not been extracted, the direct method is used for tracking, otherwise, the feature point method is used for tracking; S7, completingthe initial camera pose estimation. The invention can more precisely carry out navigation and positioning.

Description

technical field [0001] The invention belongs to the technical field of navigation and positioning, and relates to a fast monocular visual odometer navigation and positioning method that combines feature point methods and direct methods. Background technique [0002] SLAM (simultaneous localization and mapping, real-time positioning and map construction) refers to the robot moving from an unknown position in an unknown environment, positioning itself according to position estimation and maps during the moving process, and building augmented reality based on its own positioning. Quantitative maps to realize autonomous positioning and navigation of robots. As an important part of the visual SLAM method, visual odometry largely determines the accuracy and speed of the visual SLAM method. [0003] Visual odometry mainly adopts two calculation methods: feature point method and direct method. The feature method first extracts image feature points and descriptors, and then calcula...

AI Technical Summary

Problems solved by technology

Compared with the feature method, the direct method has a faster execution speed because it does not need to extract image features; it is more robust to the photometric error of the image, but it has high requirements for the internal parameters of the camera, and the performance of the algorithm decreases rapidly when there is geometric noise; Camera positioning can still be achieved in the case of image motion blur, but the robustness to large baseline motion is poor

[0004] In the prior art, there is no technical solution for using the fusion feature point method and the direct method at the same time for navigation and positioning, so how to overcome the difficulties, based on the characteristics of the feature point method and the direct method, the fusion of the feature point method and the direct method is an issue in this field. One of the research directions of technicians

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