Research On Visual Perception Algorithm Of Mobile Robot Based On Deep Learning

China is a big manufacturing country in the world,but it is not a manufacturing power.At present,many industrial production efficiencies and automation of production processes in China are still low.Therefore,many industries are in urgent need of transformation and upgrading,and robots are the key to realizing intelligent manufacturing.With the introduction of "Made in China 2025" and "Industry 4.0",robots usher in a golden age of development.A large amount of capital flows into robot research,and industrial robots have developed rapidly.In recent years,with the rapid development of online shopping business,the fast delivery of online shopping products has become the key to improving shopping experience.The use of mobile robots to achieve automatic and fast handling of goods not only saves a lot of labor costs,but also improves the speed and safety.And a vision system can greatly increase the flexibility of the robot and the accuracy of grabbing.Firstly,this paper analyzes the related visual technologies and describes the development process of each related classical visual algorithm.Secondly,it makes a reasonable analysis for vision system on robot.According to the actual requirements,a set of target object localization algorithm and attitude estimation algorithm based on deep learning is proposed.It mainly includes the acquisition of the depth map of the target object,the determination of the three-dimensional location and the attitude estimation.The RGBD matching technology is used to project the depth map acquired by the TOF camera into the pixel coordinate system of the color camera,which realizes the unification of the two cameras' coordinate system.The interpolation algorithm is then used to obtain a high-resolution depth fusion map to estimate the position of the object in three-dimensional space.This paper uses the target detection network algorithm to detect the object.In order to adapt to the computing power and memory resources of the embedded platform,the classical convolution structure in the network is replaced.The fusion depth map is used to optimize the anchor box mechanism in the original network without increasing the calculation.In order to estimate the attitude of the target object,this paper applies the semantic segmentation network to identifying the smooth surface of the object.After extracting the plane of the surface,spatial model of the object is built and the plane normal vector is determined.So the attitude information of the object can be obtained.The robot arm can move to the top of the object,and the nozzle at the end of the robot arm is attached to the surface of the object along the normal vector direction,so that the robot arm can grasp the target object.Finally,this paper has carried out repeated experiments and tests on the designed visual algorithm scheme in different environments.The visual fusion algorithm module,the target detection algorithm module and the attitude estimation module are tested repeatedly.The experimental results show that the proposed target detection algorithm can achieve 86.93 mAP on the dataset.The attitude estimation algorithm has strong stability in most complex environments.The target object can find its location and analyze the final pose of the object.The algorithm adopts parallel design,and the running time of whole algorithm is about 1.2s.