Study On The Method Of Environment Image Recognition For Autonomous Mobile Robot

With the development of computer technologies and human sensation researches,the advanced control of intelligent robot has become a hot spot in academic.Giving the robot the function of look is one of the main questions of robot vision research. And the robot use vision to recognition the environment is a complex process.Therefore, the research of real-time vision recognition system with a high recognition rate, a strong anti-noiseability and highly robust performance has a vital significance and the theoretical value.First of all in this dissertation,the environment image'pretreatment methods are studied:(1) The enhancement methods of original environment images are studied.Though using piecewise-linear function and histogram processing,the light-dark images'contrast are enchanced.Then, for the fuzzy images with noise,comparing several general used de-noising method,an improved wavelet mid-threshold denoising method is proposed.The experiment result shows that this method can achieve superior de-noising performance.(2) Image edge extraction based on a variety of templates and binarization processing methods are studied,and the experiment result is compared and analyzed. This process is well prepared for follow-up image recognition.Secondly,the environment images recognition and classification methods are studied:(1) Using SIFT(Scale-invariant feature transform)feature extraction methods, building the image pyramid, extracting image's edge features and then the recognition between the two images is though their features' Euclidean distance.In addition,on the basis of SIFT,the dissertation has two improvemwnt in terms of real time:first,a new threshold is added to the process of the match between the two features,in order to exclude directly the features which are not matched,so that the complex 128 deviation square operation is no need to be calculated.Second,it is not need to continue until the last enough small group when the image pyramid is established,and though the experiment it is found that the total number of features will no longer be increased over the first 4 group.Therefore,this method saves running time.Simulation experimental results show the two methods are effectiveness.(2) Neural network classification method based on Gabor filter and moment invariant is studied in this dissertation.The principle of image feature extraction based on Gabor filter as well as moment invariant is described.32 local features are extracted though Gabor filter and 7 global features are extracted though moment invariant.Then,the 39 features value is learned by neural network and adjustable parameters of neural network are trained to optimize.Simulation results show that the algorithm improves the recognition accuracy at the same time also cuts down the recognition time.This algorithm is able to meet the requirement to recognize the environment for intelligent mobile.