Research On Road Elevation Estimation Method In Front Of Vehicle Based On LiDAR

Road surface is the main source of vehicle excitation,so road surface input directly affects the ride comfort,ride comfort,and handling stability of a vehicle.An effective way to improve these performance is to adjust the vehicle’s active or semiactive suspension system through preview control.The preview control method is divided into inter axle preview control and pre axle preview control.Inter axle preview control controls the rear suspension of the vehicle by sensing the vibration of the vehicle’s front wheels.Control delays can be caused by factors such as control algorithm calculations,sensor acquisition data delays,and actuator response time.Front axle preview control can effectively solve the above problems,but it requires obtaining road information in front of the vehicle as input.At present,the research on the vehicle’s front axle preview control focuses on the development of control algorithms,while the research on obtaining the road elevation information in front of the vehicle is less.Therefore,this paper studies the road surface elevation estimation method in front of a vehicle,and estimates the road surface elevation information in front of the vehicle in real time.The obtained road surface elevation information can be used in the vehicle’s front axle preview control system to improve various performance of the vehicle during driving.Therefore,this paper proposes a vehicle front road elevation estimation system.The system uses solid state LiDAR(solid state Light Detection And Ranging)and IMU(Inertial Measurement Unit)as sensors,and the vehicle’s motion in threedimensional space is estimated through a tightly coupled state estimation system,and an elevation map of the road ahead of the vehicle is established considering the motion uncertainty.Finally,it is verified by experiments.The effectiveness of this algorithm.The main research work of this paper is as follows:1.Build a hardware platform and develop software algorithms for the system proposed in this article.Establish a laser sensor model,an IMU measurement model,and a kinematics model.The lidar and IMU used in this article are calibrated in spatial position and synchronized in time.2.In view of the poor accuracy of the solid-state LiDAR odometer used in this paper,this paper corrects the motion distortion of the LiDAR data and extracts the features of the original LiDAR points by calculating the curvature.Considering the small FOV of the solid-state lidar,the radar reflectivity is used as a way to extract feature points to expand the number of feature points and avoid the degradation of the odometer.3.In order to further improve the accuracy and robustness of vehicle pose estimation,avoid the defects of single sensor pose estimation.This paper proposes an algorithm based on the tightly coupled lidar and inertial measurement unit.Use the error state Kalman filter to fuse the positioning information from the lidar and the information of the IMU and use the iterative Kalman filter to solve the problem.On the premise of ensuring real-time performance,a more accurate Kalman gain can be obtained to avoid falling into a local optimum.solution,improving the solution accuracy.4.In order to improve the accuracy of road surface elevation estimation,this paper considers the impact of the noise of laser radar sensors and the uncertainty of pose estimation on road surface elevation estimation.Based on the lidar sensor model,a road surface elevation map in front of the vehicle is constructed using Gaussian noise theory.The constructed map is predicted and updated based on the estimated vehicle posture and the uncertainty of posture estimation.Using Kalman filtering to fuse the measured and predicted values of the map,the map is iterated.On this basis,the confidence ellipse theory is used to extract the elevation value at any point on the road surface in front of the vehicle.5.In order to verify the performance of the system proposed in this paper,this paper uses real vehicle experiments to collect data on urban roads and closed parks.Use Ubuntu 18.04 and the ROS(Robot Operation System)operating system to develop algorithms based on the C++language.The vehicle pose estimation algorithm and the road surface elevation estimation method in front of the vehicle are tested respectively.After verification,the accuracy of the system proposed in this paper can reach 95% at a distance of 5m from the target object,which can meet the requirements of suspension control.