Research On Visual Localization For Mobile Robot In Urban Environment

Self-localization is a basic and key technique for mobile robot navigation. Inrecent years, with the reduction of camera cost and the rapid development of thetechniques for image processing and pattern recognition, researchers pay moreattention to the visual localizaton (also termed as “vision-based localization”). Visuallocalization for mobile robot utilizes the visual information from the camera(s) tolocalize the mobile robot. Recently, the applications for mobile robots working inoutdoor environments, especially in urban areas, are very popular. Therefore, theresearch on visual localization for mobile robot in urban environments is veryimportant both in theoretical and practical applications.The dissertation focuses on visual localization for mobile robot in urbanenvironments. A localization scheme combining the satellite map of mobile robotworking area and images captured by an onboard camera is proposed. The satellitemap is used to generate the building boundaries from top-down view, and the cameraimages are utilized for building reconstruction from horizontal view. The proposedmethod can determine the absolute position of the robot in the2D satellite map bymatching the two results obtained in the above.To realize the localization scheme above, a line-based3D reconstruction methodis proposed. The point-based3D reconstruction methods are popular. However,existing point-based methods are low-accuracy, high-computation, and can notrepresent the scene exactly. Compared with point features, line features are morerobust, and insensitive to lighting condition or shadows. The line-based methods canlead to low computational cost because of their small amount and robustness.However, the matching of line features between views is a difficult issue in computervision. To this end, a structure named Multilayer Feature Graph (MFG) is firstlyproposed in this dissertation. With the aid of the geometric relationships betweendifferent features, MFG can find the line correspondences between two viewssuccessfully, and reconstruct line features and vertical planes in further. Besides, MFG is an effective method to facilitate the robot scene understanding byrepresenting the scene as different related key features, such as points, line segments,lines and vertical planes.The main work consists of two parts: the design and construction of MFG,localization algorithms based on MFG. A feature fusion method is discussed in theMFG construction part. While in the localization algorithms, an automatic buildingboundary generation method from high-resolution satellite map is developed firstly,then a feature weighted localization method based on an MFG is proposed. However,this method can not guarantee the correctness and uniqueness of solution. Thus, avoting-based localization algorithm based on multiple MFGs is designed, where eachMFG could provide several candidate solutions, and the final solution is determinedbased on the consensus of all the candidate ones. In general, the main work in thedissertation is summarized as follows:(1) Design and construction of MFG. The structure of MFG and the extractionmethods for the features in MFG are proposed. A feature fusion method for MFGconstruction is developed based on the geometric relationships between features inMFG. MFG can help us to find the line correspondences between two views, usingthat we can realize the scene reconstruction and understanding well.(2) An automatic building extraction method from high resolution satellite map.By analyzing the characters of building and non-building regions in satellite map, anovel automatic and time-efficient building boundary extraction method, with the aidof corresponding ordinary map, is proposed. This method was implemented andtested on the Google satellite maps. The physical experiments demonstrated theaccuracy and efficiency of the method.(3) Feature-weighted map query method for localization. A feature-weightedvisual localization method is proposed based on a single MFG and2D buildingboundary map. This method converts the localization task into an optimizationproblem. The location solution can be obtained by solving the optimization problem.The physical experiments demonstrated that the method can localize the robotsuccessfully in most cases. However, the theoretical analysis and physicalexperiments show that, in some complex situations, especially when there are similar buildings in the surroundings, this method can not guarantee the uniqueness of thesolution, even leads to wrong results.(4) Voting-based visual localization method. This method is an improvement ofthe feature-weighted algorithm above. Multiple MFGs generated from camera framesare utilized in the voting-based method, with each MFG providing multiple candidatesolutions. The final localization solution is determined based on the consensus of thecandidate ones. Physical experiments demonstrated that, compared with the featureweighted method, the voting-based algorithm can improve the probability ofcorrectness and the localization accuracy.