Research On Autonomous Ground Vehicle Pose Estimation Method Based On VINS-Mono

Autonomous ground vehicles require real-time and stable acquisition of accurate vehicle posture,which is a necessary condition for achieving autonomous navigation and motion.A position and attitude estimation method based on visual inertial fusion has made significant progress in research and practical applications in the field of autonomous ground vehicle position and attitude estimation.However,the inertial pose estimation method based on monocular vision has a problem in that the scale is not observable under certain operating conditions of ground vehicles,leading to degraded pose trajectory.This paper aims to improve the framework of Monocular Visual Inertial Navigation System(VINS Mono)and address the challenges encountered in the application of visual inertial fusion to ground vehicles.The main contributions of this paper are as follows:Firstly,a simulation vehicle and outdoor simulation environment based on Gazebo/ROS for autonomous ground vehicles are built,which enables the collection of data for autonomous navigation.Additionally,a data acquisition platform based on the Ackermann Motion Chassis has been built,including cameras,wheel speedometers,GPS,and IMU sensors,to collect environmental and motion data of vehicles in real-world scenarios.Utilizing multiple serial ports and threads to achieve high-frequency communication of sensor data,controlling chassis motion through the inverse kinematics solution of the Ackermann vehicle,and collecting data during the motion process.Next,the camera,IMU,and wheel speed sensors in the data acquisition platform are calibrated using internal parameters based on measurement models and error models,and the sensor data are compared before and after calibration.Complete spatiotemporal synchronization based on the camera.Integrate the data of different sensors into a complete measurement.Then,a multi-sensor fusion state estimator is constructed to clarify the measurement constraints of multi-sensors and the state variables that need to be optimized.Utilizing the complementary characteristics of multiple sensors to complete the joint initialization of the vision IMU wheel speedometer,providing an accurate initial value for the state estimation system.Finally,the algorithm in this paper is tested and compared with VINS-Mono based on simulated and actual scene data.The experimental results demonstrate that the proposed algorithm is highly effective in enhancing the accuracy of vehicle positioning,thereby outperforming VINS-Mono.