Pose Estimation And Control Of Mobile Robot Based On Multi-view Geometry

With the rapid development of the computer vision and vision sensors,vision-based pose estimation and visual servoing have gradually become one of the key technologies in the field of academic and industrial circles.Reliable visual positioning and control have important signifi-cance in many fields such as unmanned driving,unmanned storage,geological survey,etc.,and it is one of the key technologies that need to be solved in modern intelligent manufacturing.In this paper,considering the planar constraints and the uncertainty of intrinsic parameters,the visual positioning and control tasks are developed which are mainly based on multi-view geometry.Based on the multi-view geometry model,this paper considers the uncertainty of the intrin-sic parameters of the camera model and proposes a new pose estimation algorithm.Meanwhile,combined with the planar motion constraint of the vehicle,the complexity of the pose estimation algorithm is simplified.The method proposed in this paper makes use of the corresponding fea-tures between frames in multi-view to estimate the relative pose between two frames.Compared with the classical multi-view geometry based methods,this approach is suitable for both planar and non-planar scenes,and the camera can be partially calibrated.In order to solve the problem of intrinsic parameter uncertainty,this paper introduces a quasi-normalized European coordinate,which successfully realizes the parameterization of the relative pose relationship between the pixel coordinate and the world coordinate system.Furthermore,for the parametric equation,a solver is designed in this paper,and the solution of the rotational and translational information is devel-oped.Aiming at the proposed al,gorithm,this paper designs optimization algorithms including random sampling consistency and data normalization,which effectively improve the robustness of the proposed algorithm.This paper also compares the algorithm with the classical multi-view geometry based method through simulation and experimental results.The results show that the proposed method has advantages in applicable scenarios and solution accuracy,and it can still work with partially calibrated cameraBased on the nonholonomic constraint of vehicles,the kinematics model is designed.At the same time,this paper analyzes and compares different visual servo controllers,and realizes visual servoing tasks which applied with the proposed algorithm by simulation and experimental results.The visual servoing tasks mainly including trajectory tracking and target regulation.The simula-tion and experimental results int real environment show that the proposed method can successfully complete the tasks of visual servoing with partially calibrated camera.In summary,the main innovations and contributions of this paper are as follows:(1)Utilizing the techniques of computer vision and visual slam,a visual odometer was de-signed for mobile robot indoors which realize feature extraction,feature matching,optical flow tracking and pose extraction,and the accuracy is 0.5mm/frame.(2)Based on multi-view geometry,a pose estimation algorithm which considers the uncer-tainty of intrinsic parameters and both planar and non-planar scenes is proposed.And an opti-mization algorithm including random sampling consistency and data normalization is proposed for the pose estimation algorithm.The comparison with the classical multi-view geometry based methods are presented via simulation experiments.(3)Based on the nonholonomic constraint of the vehicle,the kinematics model was designed.And this paper successfully realizes the visual servoing tasks which applied with the proposed pose estimation algorithm by simulation and experimental results.