Monocular vision odometer method fusing edge features and deep learning

Abstract

The invention discloses a monocular vision odometer method fusing edge features and deep learning, and relates to the technical field of visual odometers, and aims to solve the problems that in a low-texture motion scene, few features can be extracted from an image, feature data is lost when feature matching is carried out by a visual odometer, and the pose estimation precision is reduced. The monocular vision odometer method is characterized in that firstly, an edge enhancement algorithm is designed on the basis of a Canny edge detection algorithm, an image data set after edge enhancement serves as input of a convolutional neural network and is subjected to feature extraction, output of the convolutional neural network is input into a recurrent neural network for calculation, and finally,the whole model is output to estimate the pose of a camera and optimize feature extraction. Experimental results show that the algorithm can learn more image features during model training, the accuracy of pose estimation is improved, and excellent performance is shown in a low-texture scene.

Description

technical field [0001] The invention relates to the technical field of visual odometry, in particular to a monocular visual odometry method that combines edge features and deep learning. Background technique [0002] Visual odometry is a method for estimating ego-motion from input images, which is a core module of instant localization and map building systems. Since the monocular visual odometry (Visual Odometry, VO) can determine the current position according to the feed signal of the camera, it has become a hot research field in the field of computer vision. It has a wide range of applications in autonomous driving, robotics and other fields. In recent years, visual odometry from stereo cameras has achieved great progress and has been widely used due to its reliable depth map estimation capability. However, once the distance between the camera and the baseline differs from the scale of the scene, it degenerates to a monocular distance. [0003] Different from stereo vi...

AI Technical Summary

Problems solved by technology

However, once the distance between the camera and the baseline differs from the scale of the scene, it degenerates to the monocular distance

[0003] Different from stereo vision odometry, monocular VO cannot obtain the real-scale environment map and robot motion, so it needs to use prior knowledge or information such as camera height to estimate the absolute scale, which makes monocular VO easier to produce than stereo VO Bigger drifts, more challenging

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