Obstacle Detection In Front Of Mower Based On Machine Vision And Lidar

With the development of computers and artificial intelligence,modern intelligent technology is not only used in industrial manufacturing,military,scientific research,etc.,but also has been more and more widely used in agricultural machinery.At present,the planting scale of fruit trees in our country is expanding constantly.As an important machinery of orchard mowing,mowers can replace human beings to achieve many tasks that people can’t do under heavy or bad conditions.However,in the operation process of mowers,it is necessary to avoid large blocks,fruit trees and other obstacles to ensure the stable progress of mowers.Cameras and lidars are the main sensors in obstruction detection in front of lawn mowers.The image information provided by the camera and the distance information collected by the lidar can complement each other.The fusion of the two data information has become a research hotspot at this stage.In this paper,through the fusion of camera and lidar data,the two key technologies in lawn mower obstacle avoidance,namely the joint calibration of camera and lidar and the detection and positioning of obstacles in front of the lawn mower,are studied in depth.(1)The coordinate system was established and the calibration of the binocular camera and lidar was completed.The vehicle body is used as the world coordinate to explain the relative position of the sensor,and the sensor coordinate system based on lidar and camera is established;The MATLAB software is the main operating platform,and the camera calibration is performed using Zhang Zhengyou’s MATLAB calibration toolbox,and the distortion correction is completed.The Lidar coordinate system is converted to the pixel coordinate system for later data fusion.(2)Visual image and lidar data processing.Image enhancement is realized by gray conversion,gray stretch and histogram equalization.The image is denoised by Gaussian filtering algorithm.The adaptive threshold based on OTSU and K-means segmentation algorithm based on feature space are used to segment the image.Finally,k-means clustering segmentation algorithm is used,and the preliminary segmented obstacles are processed by morphological method.The processing results show that k-means algorithm has better effect on obstacle detection.The SURF stereo matching algorithm is used to detect the feature points of the segmented image and process the stereo matching.The RANSAC algorithm is used to eliminate the wrong matching points.The original data of obstacles collected by lidar are filtered and denoised.The k-means clustering algorithm is used to analyze the lidar scanning data,and the obstacle contour information is obtained.Obstacle location information was further determined by curve fitting.(3)Lidar data and visual image fusion processing.Data fusion was performed between lidar data and binocular camera images using the perspective transformation principle,and a fusion algorithm experiment was designed.The experimental results show that the detection of obstacles in front of the mower can be realized more accurately by fusing the data of ZED binocular camera and lidar.