Research On Service Robot Visual Tracking Technology

Robotics is a multi-field science,mainly related to machine vision,environmental perception,kinematic control and so on.Machine vision uses vision sensors and computers to replace the human eye and brain to sense and understand the environment.Moving target tracking is an important research direction of machine vision.It has extensive applications in visual navigation,human-computer interaction,intelligent transportation and motion analysis.In this paper,the moving object detection and tracking algorithm in the process of visual tracking of service robot is researched,and the target detection and tracking algorithm is verified on the Pioneer robot platform with ROS(Robot operating system).The main contents of this paper are:(1)Target tracking algorithm based on deep learning is applied to service robot.We do more search on RCNN series algorithms and SSD algorithms.Firstly,we introduced RCNN algorithm,other RCNN series algorithms such us SPP-Net,Fast RCNN,and Faster RCNN sequentially optimize the network.The experiment shows that the test time of a single picture can be reduced from 50 s to 0.198 s.However,since the service robot processor is an embedded system,the data processing speed is slow.While RCNN series algorithm has a large amount of calculation,which can not meet the real-time requirement,so SSD(Single Shot MultiBox Detector)algorithm is finally selected for target detection and recognition.We verify the algorithms on the deep learning platform Caffe,the experiment shows that the SSD algorithm has high accuracy and speed,which can be applied to restaurant service robot visual tracking tasks.(2)A long-term target tracking TLD algorithm is studied.The algorithm consists of three modules: a tracker module,a detector module,and a learner module.The tracker part of the original TLD algorithm uses the optical flow method based on gray features.The algorithm can not track the target well when the target encountered occlusion,illumination change and deformation.Therefore,this paper improves the tracker of TLD algorithm with adaptive kernel correlation filtering algorithm based on HOG features;The improved algorithm is verified on the dataset and the real-time video stream,and the optimization algorithm is verified by three indicators of center offset error,accuracy,and recall rate.(3)Build visual tracking robot.Firstly,the two-wheeled differential motion model of Pioneer3-DX robot,then the SSD algorithm and the improved TLD algorithm are validated through the robot operating system(Robot operating system).It can be seen from the experiment that the improved algorithm can achieve accurate tracking under the conditions of occlusion and illumination changes.The final design Qt interface makes the system have better human-computer interaction.This research project studies the detection,recognition and tracking of moving objects in the process of visual tracking of service robots.The innovation of this paper:Firstly,the deep learning SSD algorithm is applied to target detection;Secondly,the tracker module in the TLD target tracking algorithm is improved by using adaptive kernel-dependent filtering algorithm to replace pyramid optical flow method The improved algorithm can achieve better tracking in the case of changes in illumination and scale changes.