Research On Analysis Of Unstructured Road Environment And Detection Of The Accessible Region Based On Monocular Vision

Vision navigation system is one of the research hotspots in machine vision and artificial intelligence,which is widely used for autonomous mobile platforms such as robots,intelligent vehicles and so on because of its low cost and rich color information.Important information of road safety area,relative position and so forth can be satisfying acquired through effectively understand and perceive the current road environment by the visual system.At present,it is relatively mature for the research on visual perception of structured road.In contrast,unstructured road environment is normally complex and diverse,lack of road signs and boundary,and road characteristics are complex and instability.What is more,environmental interferences such as illumination,road type and scene change irregularly.These make its related research a big challenge.In recent years,with the growing high demand for intelligence of unmanned platforms,it is more and more significant for accurately understanding and analyzing complex road environment with high real-time and robustness.This paper has carried out deep study on the analysis of unstructured road environment and the detection method of the accessible region based on monocular vision.Firstly,some pre-processing is carried out for the common shadow interference in road image.One method of road shadow detection with Gaussian mixture mode(GMM)based on color saliency space and gradient field is proposed,GMM is used to detect road shadow region by constructing color significant space and gradient field descriptors of road shadow.After acquiring the shadow area,another method for road shadow removal based on adaptive variable scale region compensation operator is further proposed.According to the operator constructed,illumination compensation is adaptively done for the shadow region in the multi-dimensional color space,realizing the road shadow removal.Experimental results show that the proposed methods of road shadow detection and removal have nice feasibility and adaptability,and is of certain reference value to the problem of road shadow preprocessing.Secondly,the analysis and estimation methods of the unstructured road environment are discussed.The idea of detecting the relatively stable vegetation information in road environment is proposed at another aspect this paper,to a certain extent,which can restricted impassable area of unstructured road,then one method for vegetation detection in unstructured road environment based on Gaussian kernel SVM is presented,effectively estimating relatively safe feasible region on the limit of vegetation region,Experiments result show that the method,in a certain degree,ensures the safe driving for autonomous mobile system in complex environment.Finally,road scene of unstructured environment is further explored on the basis of feasible region estimation,one method for road type recognition with SVM multi classifier based on gray level co-occurrence matrix(GLCM)and gradient orientation histogram(HOG)is proposed.Through the construction of GLCM and HOG isomorphism descriptors,four kinds of common road types are classified by SVM multi classifier.Experimental results show that the proposed method can be well distinguished from current unstructured road types,and has a certain adaptability,which probably provides control decision and environmental features selection for autonomous mobile platform and has a certain reference value for the unmanned system effectively analyzing and understanding current road environment.