Research And Design Of Lidar Perceptual Target Detection And Tracking Algorithm

Lidar target detection and tracking is the core link of 3D sensing.The accuracy of target detection and the accuracy of target tracking jointly determine the upper limit of the 3D sensing effect.On the one hand,the target detection method based on the lidar bird's eye view pioneeringly applies the two-dimensional image detection method to the three-dimensional target detection,which has been widely used in the field of lidar target detection.However,in the process of projecting a point cloud into a bird's eye view,the method loses the height information of the point cloud and the three-dimensional spatial position information between the laser points.The projected image of the target is easily confused with the surrounding environment,resulting in low accuracy of target detection.On the other hand,the target tracking algorithm based on extended Kalman filter solves the problem that linear Kalman filter(KF)cannot be applied to nonlinear systems,but the algorithm requires solving the Jacobian matrix has a large amount of calculation.When the nonlinear method is used to approximate the nonlinear system in a linear manner,high-order terms are lost,causing a problem of low tracking accuracy.The above two problems have become the main bottlenecks that restrict the improvement of Li DAR sensing effect.Through research,this paper comprehensively analyzes perceptual algorithms applied to lidar target detection and tracking.Based on this,a convolutional neural network target detection algorithm(G-Net)based on point cloud geometric features is designed.Mann filter(UKF)target tracking algorithm(UKF-T).Among them,the main advantages of G-Net are(1)the feature information is automatically extracted from the geometric information of the point cloud through the feature extraction network,thereby avoiding the loss of the height information and spatial position information of the point cloud;(2)the use of 2D volumes Convolutional network U-Net is used as the backbone network to encode and decode the extracted features to avoid the problem of large computation and time consuming caused by3 D convolution.The main advantages of UKF-T are(1)the amount of calculation increases linearly with the state dimension,and the amount of calculation is small;(2)the unscented transformation is applied to Kalman filtering,that is,the probability distribution of the original system state is simulated by generating key points,A high-order approximation is achieved.This paper improves the perception effect of lidar by improving the above aspects.The improved lidar target detection algorithm G-Net in this paper improves the accuracy of the target detection algorithm based on the bird's-eye view on the 3D car category in theKITTI dataset by an accuracy of 24.18%;the target tracking algorithm UKF-T proposed in this paper is in UDACITY Compared with the extended Kalman filter target tracking algorithm on the data set,the accuracy is improved by 39.63%.In real road test scenarios,the surrounding targets can also be accurately detected and continuously tracked,which shows the superiority of the algorithm in this paper.