Active camera repositioning method based on multi-plane joint pose estimation

Abstract

The invention relates to an active camera repositioning method based on multi-plane joint pose estimation. The active camera repositioning method comprises the following steps: extracting an effectiveplane area image set sum of scenes in a current observation image T and a reference observation image R; establishing a matching relationship in the effective plane area image set sum; obtaining a camera relative pose Pi guided by each group of matching planes; and fusion of all camera relative poses Pi is carried out to obtain guidance camera motion information.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence and computer vision, relates to active vision technology, and specifically relates to an active camera relocation method based on multi-plane joint pose estimation. Background technique [0002] Active camera repositioning aims to physically and truly restore the camera's six-degree-of-freedom pose to the same as when shooting the reference image. It plays an important role in environmental monitoring, preventive protection of historical and cultural heritage, and detection of small changes. It is an active An important application of vision technology [1]. The active camera relocalization process includes the relative pose estimation of the camera and the dynamic adjustment of the camera, and the adjustment of the camera is completed by the robot platform. [0003] The current state-of-the-art active camera relocation algorithm has achieved great success in the task of detecting small cha...

AI Technical Summary

Problems solved by technology

[0004] However, when the lighting conditions (direction and intensity) of previous observations are different, the monitoring results are not satisfactory

Significant differences in illumination will change the appearance of the scene (especially the ubiquitous three-dimensional structure scene), and then the feature point descriptors in the image involving pose estimation will change, resulting in the failure of camera pose estimation

In addition, if the background (non-monitoring object area) in the observation scene changes greatly, for example, the vegetation near the monitored ancient building may change drastically in different seasons (even structural changes), which will significantly increase the probability of mis-matching feature points in the image. Quantity, which seriously affects the accuracy of relocation results

The above two situations that are common in actual tasks will seriously damage the accuracy of active camera relocation, resulting in unreliable actual monitoring results, which cannot support outdoor relocation operations under uncontrollable environmental conditions

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