Virtual 3D City Model for Navigation in Urban Areas

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.

[1]  Philippe Bonnifait,et al.  Data fusion of four ABS sensors and GPS for an enhanced localization of car-like vehicles , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[2]  Michel Dhome,et al.  Real Time Localization and 3D Reconstruction , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  C. M. Wang,et al.  Location estimation and uncertainty analysis for mobile robots , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[4]  Maan El Badaoui El Najjar,et al.  A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering , 2005, Auton. Robots.

[5]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[6]  Yutaka Takase,et al.  Generation of Digital City Model , 2003 .

[7]  Jake K. Aggarwal,et al.  Mobile robot self-location using model-image feature correspondence , 1996, IEEE Trans. Robotics Autom..

[8]  James R. Bergen,et al.  Visual odometry for ground vehicle applications , 2006, J. Field Robotics.

[9]  Robert B. Noland,et al.  A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport , 2006, J. Intell. Transp. Syst..

[10]  Trung-Dung Vu,et al.  Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[11]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization, Mapping and Moving Object Tracking , 2007, Int. J. Robotics Res..

[12]  Eduardo Mario Nebot,et al.  Recursive scan-matching SLAM , 2007, Robotics Auton. Syst..

[13]  D. Meizel,et al.  GPS/GIS localization for management of vision referenced navigation in urban environments , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[14]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[15]  Patrick Rives,et al.  Accurate Quadrifocal Tracking for Robust 3D Visual Odometry , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[16]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[17]  Philippe Bonnifait,et al.  An experiment of a 3D real-time robust visual odometry for intelligent vehicles , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[18]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[19]  Chih-Ming Wang.  Location Estimation and Uncertainty Analysis for Mobile Robots , 1990, Autonomous Robot Vehicles.

[20]  Mohinder S. Grewal,et al.  Global Positioning Systems, Inertial Navigation, and Integration , 2000 .

[21]  Christian Laugier,et al.  Geometric and Bayesian models for safe navigation in dynamic environments , 2008, Intell. Serv. Robotics.

[22]  Sebastian Thrun,et al.  Multi-robot SLAM with Sparse Extended Information Filers , 2003, ISRR.

[23]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[24]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[25]  Kostas Daniilidis,et al.  Monocular visual odometry in urban environments using an omnidirectional camera , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Peter K. Allen,et al.  Localization methods for a mobile robot in urban environments , 2004, IEEE Transactions on Robotics.

[27]  Philippe Bonnifait,et al.  VEHICLE LOCALIZATION IN URBAN CANYONS USING GEO-REFERENCED DATA AND FEW GNSS SATELLITES , 2007 .

[28]  Takeo Miyasaka,et al.  Ego-motion estimation and moving object tracking using multi-layer LIDAR , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[29]  Kurt Konolige,et al.  Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[30]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[31]  Michel Dhome,et al.  Towards geographical referencing of monocular SLAM reconstruction using 3D city models: Application to real-time accurate vision-based localization , 2009, CVPR.

[32]  Martin Dodge,et al.  Towards the Virtual City: VR & Internet GIS for Urban Planning , 1998 .

[33]  Éric Marchand,et al.  Real-time markerless tracking for augmented reality: the virtual visual servoing framework , 2006, IEEE Transactions on Visualization and Computer Graphics.

[34]  Sylvie Servigne,et al.  Panorama des potentialités SIG en 3 dimensions : vers des modèles virtuels 3D de villes , 2008 .

[35]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[36]  Michel Dhome,et al.  Outdoor autonomous navigation using monocular vision , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[37]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[38]  Ian D. Reid,et al.  Mapping Large Loops with a Single Hand-Held Camera , 2007, Robotics: Science and Systems.

[39]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[40]  Philippe Bonnifait,et al.  Autonomous navigation in urban areas using GIS-managed information , 2008 .