Visual SLAM method based on semantic optical flow and inverse depth filtering

Abstract

The invention relates to a visual SLAM method based on semantic optical flow and inverse depth filtering, and the method comprises the following steps: (1) a visual sensor collects an image, carries out the feature extraction and semantic segmentation of the collected image, and obtains extracted feature points and a semantic segmentation result; and (2) according to the feature points and a segmentation result, performing map initialization by using a semantic optical flow method, removing dynamic feature points, and creating a reliable initialization map; and (3) evaluating whether 3D map points in the initialized map are dynamic points or not by adopting an inverse depth filter, and expanding the map according to an evaluation result of the inverse depth filter. And (4) continuing to perform tracking, local mapping and loopback detection in sequence for the map expanded by the depth filter, and finally realizing dynamic scene-oriented visual SLAM based on semantic optical flow and inverse depth filtering.

Description

technical field [0001] The present invention relates to a visual SLAM method based on semantic optical flow and inverse depth filtering, which is a new visual SLAM method that combines semantic optical flow and inverse depth filtering technology, and is suitable for solving the problem of traditional visual SLAM systems in high dynamic scenes Failure and lack of understanding of the scene. Background technique [0002] Simultaneous Localization and Mapping (SLAM) refers to the estimation of the pose of the robot itself through the acquired sensor data without the prior information of the environment, and at the same time constructing a globally consistent environment map. Among them, the SLAM system based on visual sensors is called visual SLAM. Because of its low hardware cost, high positioning accuracy, and the advantages of completely autonomous positioning and navigation, this technology has attracted wide attention in the fields of artificial intelligence and virtual re...

AI Technical Summary

Problems solved by technology

[0005] The problem solved by the technology of the present invention is: to overcome the deficiencies of the existing technology, aiming at the problem that the visual positioning system of the system is susceptible to interference under dynamic scene conditions, a visual SLAM method based on semantic optical flow and inverse depth filtering is provided to improve the SLAM system to cope with dynamic scenes ability, improve the system's ability to understand the scene, and improve the system's positioning accuracy in dynamic scenes

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