PTAM improvement method based on ground characteristics of intelligent robot

Abstract

The invention discloses a PTAM improvement method based on ground characteristics of an intelligent robot. The PTAM improvement method based on ground characteristics of the intelligent robot comprises the steps that firstly, parameter correction is completed, wherein parameter correction includes parameter definition and camera correction; secondly, current environment texture information is obtained by means of a camera, a four-layer Gausses image pyramid is constructed, the characteristic information in a current image is extracted by means of the FAST corner detection algorithm, data relevance between corner characteristics is established, and then a pose estimation model is obtained; two key frames are obtained so as to erect the camera on the mobile robot at the initial map drawing stage; the mobile robot begins to move in the initializing process, corner information in the current scene is captured through the camera and association is established at the same time; after a three-dimensional sparse map is initialized, the key frames are updated, the sub-pixel precision mapping relation between characteristic points is established by means of an extreme line searching and block matching method, and accurate re-positioning of the camera is achieved based on the pose estimation model; finally, matched points are projected in the space, so that a three-dimensional map for the current overall environment is established.

Description

Technical field [0001] The invention belongs to the field of robot vision and relates to an improvement of a PTAM algorithm based on ground features. Background technique [0002] As the relationship between robots and humans gets closer, the related technologies of intelligent robots have received great attention. Simultaneous Localization and Mapping (SLAM) of mobile robots is one of the most mainstream positioning technologies for smart mobile robots. It is actually a motion estimation problem. It uses the internal and external data obtained by the sensor to calculate the position of the mobile robot at a certain moment, and at the same time establishes the map model it depends on. Vision-based SLAM belongs to the research category of vision measurement. Because the vision sensor has its own unique advantages: small size, light weight, low price, easy installation, and very rich external information extracted. These advantages further promote the current research and applica...

AI Technical Summary

Problems solved by technology

However, the unimproved PTAM algorithm also has the problem of being unable to establish a metric map, and has strict restrictions on the movement of the camera

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