Visual SLAM method and system based on full convolutional neural network in dynamic scene

Abstract

The invention provides a visual SLAM method and system based on a full convolutional neural network in a dynamic scene. The method comprises the following steps: acquiring an image data set; constructing a full convolutional neural network model according to the image data set; performing semantic segmentation on a monocular real-time image currently collected by a camera by using the full convolutional neural network model to obtain a semantic tag image; removing dynamic feature points of the monocular real-time image according to the semantic tag image to obtain static feature points of the monocular real-time image; and estimating the pose of the camera according to the static feature points. Through the method provided by the invention, a dynamic target can be accurately identified and semantic segmentation can be completed, the accuracy and robustness of camera tracking are effectively improved, and the positioning and mapping precision of visual SLAM in a dynamic scene is improved.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a visual SLAM method based on a fully convolutional neural network in a dynamic scene and a visual SLAM system based on a fully convolutional neural network in a dynamic scene. Background technique [0002] Simultaneous Localization And Mapping (SLAM) refers to the process of a robot estimating its own pose and constructing an environmental map through its own sensors in an unfamiliar environment. It is a prerequisite for many robot application scenarios, such as path planning, Collision-free navigation, environment awareness, etc. Visual SLAM refers to the perception of using visual information to estimate the camera's own pose and build a three-dimensional map of the environment. [0003] In the prior art, the relative displacement of the two frames can be estimated according to the matching of feature points between two adjacent frames of the input image, so a...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the technical problem in the prior art that slow-moving dynamic objects lead to deviations in the calculation of camera poses, resulting in inaccurate positioning of the entire visual SLAM system, the present invention provides a visual SLAM method and method based on a fully convolutional neural network in a dynamic scene. A visual SLAM system based on fully convolutional neural network in dynamic scenes. This method can effectively improve the accuracy and robustness of camera tracking, and improve the positioning and mapping accuracy of visual SLAM in dynamic scenes.

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