Unmanned aerial vehicle pose estimation method based on visual inertia polarized light fusion

Abstract

The invention discloses an unmanned aerial vehicle pose estimation method based on visual inertia polarized light fusion, which adopts the combination of visual measurement and inertia measurement, and can well solve the problems of low output frequency, poor robustness and unobservable scale information of pure visual measurement after the visual measurement and the inertia measurement are fused.Meanwhile, the constraint among a plurality of images in visual measurement can also effectively inhibit integral drifting in inertial measurement and correct zero offset of inertial measurement. Onthe basis of vision measurement and inertia measurement, the course constraint of the polarized light compass is also added, the polarized light compass obtains the course information of itself by simulating the structure and function of sensing polarized light by desert ants. The method has the advantages of no accumulation of errors along with time and high stability, and can provide stable course constraint for a navigation sensor of an unmanned aerial vehicle. Compared with an existing visual inertia combination, the visual inertia polarized light fusion-based unmanned aerial vehicle poseestimation method provided by the invention can greatly improve the pose estimation precision of the unmanned aerial vehicle.

Description

technical field [0001] The invention relates to the technical field of motion estimation, in particular to a UAV pose estimation method based on visual-inertial polarized light fusion. Background technique [0002] In the UAV pose estimation, the monocular vision system tracks the common features between multiple images, and uses the multi-view geometric relationship to solve the position and attitude relationship between the images. It has high precision under certain conditions; the inertial measurement unit (IMU) has good autonomy, is less affected by the environment, and still has stable performance under high dynamic conditions, but the error will quickly diverge with time. There is a strong complementarity between IMU and vision. The fusion of vision and IMU can well solve the problems of low output frequency, poor robustness and unobservable scale information of pure vision system. At the same time, the constraints between multiple images of the visual sensor can als...

AI Technical Summary

Problems solved by technology

[0003] The present invention provides a UAV pose estimation method based on visual-inertial polarized light fusion, which is used to overcome defects such as accumulation of course error over time in UAV pose estimation in the prior art

Images