Research On Unmanned Vehicle Visual Preception System Based On Huawei AI Handware

The perception system is the basis for unmanned vehicles to realize various functions of autonomous driving.Especially,the perception technology based on visual sensors is widely used in the object detection and localization functions of unmanned vehicles due to the advantages of low cost.At present,vision-based object detection has a large amount of calculation and is difficult to be used in vehiclemounted computing platforms with limited performance.The assumption of scene rigidity is typical in SLAM algorithms.Therefore,these systems often have poor accuracy in real traffic scenes containing dynamic objects.Moreover,the intelligent hardware platform is almost monopolized by foreign countries,and the development of an independent intelligent hardware platform is imminent.Based on the Atlas 200 DK intelligent hardware platform,this paper uses a monocular camera to study the problem of lightweight object detection and dynamic scene SLAM for unmanned vehicles.Firstly,this paper studies lightweight object detection based on convolutional neural networks.In order to reduce the amount of calculation for convolution operations,this paper uses deep separable convolution instead of standard convolution,and uses Mobile Netv2 to streamline the YOLO network structure.Improved Mobile Net-YOLO can be better used in the embedded platform.The speed in Atlas 200 DK has increased by about 40%.Secondly,the visual odometer system was built in this paper.The transformation relationship between the world coordinate system and the pixel coordinate system is obtained through the pinhole camera model.The kinect2 camera used in this experiment was calibrated by studying the camera calibration method.ORB algorithm is used to extract and describe image feature points,and the uniform distribution of ORB feature points is improved through the quadtree.RANSAC is used to optimize the feature matching results.According to the feature matching relationship between images,through epipolar constraints and Pn P method completes the pose estimation of the camera.Finally,in order to improve the positioning accuracy of classic SLAM in dynamic scenes,Mobile Net-YOLO is used to segment dynamic objects to improve the SLAM front-end,and use the segmented static background frames for feature extraction and pose estimation in this paper.This paper verifies the real-time performance of this system on Atlas 200 DK,which can reach a running speed of about 10 Hz.The TUM data set for dynamic scene is used to compare the positioning accuracy of our system and ORB-SLAM3.The experimental results show that the system in this paper effectively improves the positioning accuracy in dynamic scenes.In addition,this paper also builds an outdoor data collection platform to evaluate the positioning accuracy of our system in outdoor scenes.