LI Haifeng, HU Zunhe, LIU Jingtai. Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation[J]. 机器人, 2016, 38(3): 311-321. DOI: 10.13973/j.cnki.robot.2016.0321
引用本文: LI Haifeng, HU Zunhe, LIU Jingtai. Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation[J]. 机器人, 2016, 38(3): 311-321. DOI: 10.13973/j.cnki.robot.2016.0321
LI Haifeng, HU Zunhe, LIU Jingtai. Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation[J]. ROBOT, 2016, 38(3): 311-321. DOI: 10.13973/j.cnki.robot.2016.0321
Citation: LI Haifeng, HU Zunhe, LIU Jingtai. Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation[J]. ROBOT, 2016, 38(3): 311-321. DOI: 10.13973/j.cnki.robot.2016.0321

Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation

Enhanced Geometric Map: a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation

  • 摘要: To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map (EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model, and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping (SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust.

     

    Abstract: To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map (EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model, and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping (SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust.

     

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