Research on integrated vehicle localization by integrating vision and kinematics
The research purpose of this article is to use monocular cameras,consumer grade Inertial Measurement Units,and vehicle chassis data as inputs to the positioning system in the event of global positioning system failure in urban canyons,tunnels,and other areas,to provide positioning information for intelligent vehicles.In this paper,a dynamic tire circumference model is proposed first,and the optimization values of the parameters to be calibrated are solved by establishing the nonlinear least squares problem for the relevant parameters,and the kinematic based vehicle odometer is improved.Afterwards,the improved odometer data was integrated into the scale factor initialization process of the positioning system front-end,reducing the adverse effects of Inertial Measurement Units noise during vehicle startup and improving the stability of system initialization.In the backend part,for the fusion of multi-sensor data,non-linear optimization is carried out.The vins_wheel proposed in this article was compared with the vins_fusion algorithm through real vehicle experiments,demonstrating the improvement of the algorithm in terms of initialization stability and vehicle positioning accuracy.
simultaneous localization and mappingmulti-sensor fusionwheel odometryvehicle kinematics
NETL
NSTL
万方数据
维普
