Research On Obstacle Avoidance And Mapping Technology Of Intelligent Vehicle Based On Lidar

In recent years,driverless environment perception technology has become a research hotspot,and the use of sensors to achieve real-time obstacle detection and positioning and mapping of intelligent vehicle is the basis for intelligent vehicle autonomous navigation.Lidar has become a key sensor for driverless intelligent vehicle due to its high precision and 360° to obtain the point cloud data of the surrounding environment.In this paper,Lidar and a variety of sensors are fused to research and design and test a multi-sensor intelligent vehicle obstacle avoidance mapping system based on Lidar.The main research work includes the following:1.For the obstacles detection of Lidar,on the basis of analyzing the characteristics of two-dimensional Lidar data,according to the randomness of indoor obstacles and the characteristics of uneven density of Lidar data,an adaptive threshold clustering method based on distance and density is proposed.After the preprocessing of the Lidar data through median filtering for noise reduction and coordinate transformation,the adaptive threshold is set according to the distance from the obstacle to the center of the Lidar and the change in data density,and the algorithm is verified by experiments.2.For the positioning and mapping of Lidar,the basic principle of SLAM algorithm is first studied,the probability model of SLAM system based on filtering is analyzed,the system coordinate system is modeled,and the global coordinate system,vehicle body coordinate system and sensor coordinate system are built.The odometer is built according to the kinematic model,and the Lidar scanning matching is carried out.Aiming at the problem of accumulated error in encoder odometer,a hybrid IMU odometer correction algorithm is proposed,and complementary filtering is used for data fusion to obtain attitude angle.The principle and key steps of the RBPF particle filter algorithm are studied,and the simulation experiment of the RBPF particle filter algorithm in MATLAB is carried out to study the effect of the algorithm on the location and path estimation.3.A multi-sensor intelligent vehicle experiment system based on Lidar was built,including a host computer,a chassis and a remote PC.The chassis uses STM32F103ZET6 as the processor,the L289 N motor drive drives the DC deceleration motor through PWM,measures the speed through the AB phase incremental Hall encoder,the PID controller adjusts the speed,and realizes the motion control through the kinematic model.Multiple infrared and ultrasonic sensors are distributed in the vehicle body to achieve multiple priority obstacle avoidance based on interruption.The upper computer uses ROS system as software platform to realize data transmission,coordinate transformation and odometer with chassis.Finally,based on the Gmapping algorithm,the processed Lidar data and odometer data are further fused to build a two-dimensional grid map of the environment where the intelligent vehicle is located.4.After the platform was built,motion control tests were conducted,and obstacle detection experiments and positioning and mapping experiments were carried out in the corridors and indoors of the laboratory.The experimental results verified the effectiveness of the entire obstacle avoidance and mapping system.69 figures,7 tables,68 references.