Research On Key Technologies Of Moving Object Detection Based On Laser And Vision Information Fusion

Intelligent environment perception,the purpose of which is to model the driving environment by collecting sensor data effectively,is an important part of the field of autonomous driving.Moving object detection is the key part of this process.Detecting multiple moving targets in a crowded traffic environment has always been a challenging task,facing many difficult problems such as complex scene structure,sudden lighting conditions changes,shadow interference,and dynamic background in the actual environment.The above challenges and difficulties make moving object detection a difficult problem in autonomous driving.Therefore,it is of great significance to construct a highly robust and accurate moving object detection model.This thesis combines Lidar point cloud data and visual image data,based on deep learning networks and motion detection theory,to carry out research on human-vehicle target classification and recognition and motion detection in the driving environment.The specific research content is as follows:Firstly,the related theories of Lidar and camera sensor is studied,and the collected data are analyzed.Aiming at the sparse and disordered point cloud data,which is difficult to use directly,triangular linear interpolation and RANSAC algorithms are introduced.Based on depth map,a point cloud densification and ground removal algorithm is proposed.Then,a dataset which contains picture form of the point cloud is constructed,ensuring the validity of the point cloud data.The data fusion methods are compared and discussed.Secondly,the object detection algorithm based on deep learning is studied.Taking moving objects such as people and cars,which are interested in autonomous driving,as detection targets,in view of the complex structure of the driving environment and the limitations of the computing performance of the embedded platform,we combine point cloud data and image data to design a lightweight object detection network.This network is based on multi-scale feature fusion of point cloud and visual data,multi-scale prediction target detection module and deep separable convolution feature extraction module.A feature fusion and correction module based on the attention mechanism is also applied,which effectively reduces the model size on the premise of ensuring detection accuracy.Finally,a human-vehicle target classification and motion estimation algorithm based on two kinds of data fusion algorithm is proposed.Aiming at the dynamic background of the driving environment,based on the human-vehicle target detection network,improved motion compensation and optical flow methods are applied to evaluate the motion state of human-vehicle targets in an autonomous driving environment.Based on the experiments in the KITTI dataset,it is showed that the test accuracy and robustness of the multi-source data fusion model are higher than those of single data moving object detection algorithms.