LI Haifeng, LIU Jingtai, LU Xiang. Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database[J]. 机器人, 2012, 34(5): 604-613,619. DOI: 10.3724/SP.J.1218.2012.00604
引用本文: LI Haifeng, LIU Jingtai, LU Xiang. Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database[J]. 机器人, 2012, 34(5): 604-613,619. DOI: 10.3724/SP.J.1218.2012.00604
LI Haifeng, LIU Jingtai, LU Xiang. Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database[J]. ROBOT, 2012, 34(5): 604-613,619. DOI: 10.3724/SP.J.1218.2012.00604
Citation: LI Haifeng, LIU Jingtai, LU Xiang. Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database[J]. ROBOT, 2012, 34(5): 604-613,619. DOI: 10.3724/SP.J.1218.2012.00604

Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database

Visual Localization in Urban Area Using Orthogonal Building Boundaries and a GIS Database

  • 摘要: A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric information nor appearance data. The proposed method firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with random sample consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building boundary map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.

     

    Abstract: A framework is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D map with building outlines with neither 3D geometric information nor appearance data. The proposed method firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with random sample consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building boundary map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.

     

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