Ground truth evaluation of large urban 6D SLAM

In the past many solutions for simultaneous localization and mapping (SLAM) have been presented. Recently these solutions have been extended to map large environments with six degrees of freedom (DoF) poses. To demonstrate the capabilities of these SLAM algorithms it is common practice to present the generated maps and successful loop closing. Unfortunately there is often no objective performance metric that allows to compare different approaches. This fact is attributed to the lack of ground truth data. For this reason we present a novel method that is able to generate this ground truth data based on reference maps. Further on, the resulting reference path is used to measure the absolute performance of different 6D SLAM algorithms building a large urban outdoor map.

[1]  Joachim Hertzberg,et al.  Globally consistent 3D mapping with scan matching , 2008, Robotics Auton. Syst..

[2]  K. Lingemann,et al.  6D SLAM - preliminary report on closing the loop in six dimensions , 2004 .

[3]  Paul Newman,et al.  Outdoor SLAM using visual appearance and laser ranging , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[4]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Wolfram Burgard,et al.  Towards Mapping of Cities , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[8]  E. Nebot,et al.  Autonomous Navigation and Map building Using Laser Range Sensors in Outdoor Applications , 2000 .

[9]  Eduardo Nebot,et al.  Localization and map building using laser range sensors in outdoor applications , 2000, J. Field Robotics.

[10]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Paul Newman,et al.  Using laser range data for 3D SLAM in outdoor environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[12]  Wolfram Burgard,et al.  Particle Filters for Mobile Robot Localization , 2001, Sequential Monte Carlo Methods in Practice.

[13]  Bernardo Wagner,et al.  Colored 2D maps for robot navigation with 3D sensor data , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[14]  Sebastian Thrun,et al.  6D SLAM with an application in autonomous mine mapping , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[15]  Oliver Wulf,et al.  FAST 3 D SCANNING METHODS FOR LASER MEASUREMENT SYSTEMS , 2003 .

[16]  Henrik I. Christensen,et al.  2D mapping of cluttered indoor environments by means of 3D perception , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[17]  Wolfram Burgard,et al.  Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Udo Frese Efficient 6-DOF SLAM with Treemap as a Generic Backend , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[20]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[21]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).