Research On Loopback Detection Algorithm Based On Image Information

Starting from professional automation area,robots become more and more common in modern industry and bring convenience to every aspect of daily life.The ability of self-localization and environmental maps building are the key points to create high intelligentized robot.SLAM(Simultaneous Localization and Mapping)is used to solve these problems with robotic vision system image processing.The main propose of this paper is to design a loopback detection system that corrects robots drift error caused by long running time and determines if robot has returned to a point in original path.Traditional loopback detection system extracts feature with SIFT algorithm,although it's accurate,due to large volume of calculation,and RAM wasted by descriptors,this research improves the feature extraction algorithm and descriptors,and adjusts the query logic during loopback process,then tests all the improvements.First,this research analyses the architecture of Kinect camera used to collect images,describes the fundamental principles of TOF camera,introduces how Kinect calibrate,including the transformation of 4 coordinate systems and rectifies the camera used in experiments.Following that is the introduction of image feature extraction algorithm.Then steps of most frequently used FIFT algorithm is 1.Scale-space extrema detection 2.Key point localization 3.Orientation assignment 4.Key point descriptor generation.Their principles are explained in this part,as the fundamental of algorithm improvements afterwards.After that,the descriptors of photo features during loopback detection process are extracted.Feature matching and categorization are also introduced.Then,the principle of K-Means algorithm and bag of words are analysed.Data structure and basic search methodology are included.In terms of descriptor extraction,improving from 128 D SIFT eigenvector,the binary string descriptor generated from corner detection and contrast of grey range is used.And retrieving the image search mode when performing the loopback detection improved from the original frame by frame detection mode is divided into an entire image database artificially high probability and low probability interval range,low probability of detection range for spacer frame search time is shortened.Finally,the support vector machine algorithm is used to classify the pictures in the database,and the prediction function is established to improve the algorithm efficiency of the whole loopback detection process.Finally,the article establishes four independent data sets for different environments,and separately verifies the original algorithm and the improved algorithm.Firstly,the image feature retrieval ability of SIFT algorithm is verified,and then the SIFT descriptor,improved descriptor are compared.By analyzing the execution time and the accuracy-recall rate of the algorithm,It is verified that the accuracy of the improved FAST-BRIEF operator is improved by 37% and 58% compared with the unmodified SIFT operator and the SIFT-BRIEF operator.In terms of running time,the calculation time of a single picture is shortened by about 10%.After the support vector machine is trained and predicted by the image database,the system recall rate is improved by about 44% under the premise of ensuring the accuracy index of the entire loop detection system.It is verified that the performance of the algorithm is optimized and improved through improvement.