Satellite map based quantitative analysis for 3D world modeling of urban environment

Here we present 3D world modeling and its quantitative analysis methods in urban environment. If the expensive RTK GPS cannot be prepared, it is difficult to measure the accuracy of the 3D world model due to the blackout of GPS particularly in urban environment. To cope with this difficulty, we combine to process both satellite image and point cloud to compare each other in order to represents accuracy of 3D world model. We also introduce 3D world modeling method through localization algorithm and global registration method in order to validate our quantitative analysis. In the experimental result, we describe our sensor system and evaluate the proposed quantitative analysis method using 3 different localization algorithm. Our framework is suitable of mobile mapping system in urban environment in terms of cost.

[1]  Ignacio Parra,et al.  Accurate Global Localization Using Visual Odometry and Digital Maps on Urban Environments , 2012, IEEE Transactions on Intelligent Transportation Systems.

[2]  A. Birk,et al.  3D data collection at Disaster City at the 2008 NIST Response Robot Evaluation Exercise (RREE) , 2009, 2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR 2009).

[3]  Rafael Toledo-Moreo,et al.  Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps , 2010, IEEE Transactions on Intelligent Transportation Systems.

[4]  Francisco Angel Moreno,et al.  A collection of outdoor robotic datasets with centimeter-accuracy ground truth , 2009, Auton. Robots.

[5]  Philippe Bonnifait,et al.  Matching Raw GPS Measurements on a Navigable Map Without Computing a Global Position , 2012, IEEE Transactions on Intelligent Transportation Systems.

[6]  Ahmed M. Elgammal,et al.  Satellite image based precise robot localization on sidewalks , 2012, 2012 IEEE International Conference on Robotics and Automation.

[7]  R. E. Deakin,et al.  Transforming Cartesian Coordinates X, Y, Z to Geographical Coordinates φ, λ, h , 1999 .

[8]  Myung Jin Chung,et al.  Fast point cloud segmentation for an intelligent vehicle using sweeping 2D laser scanners , 2012, 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).