A confidence measure for segment based maps

Map confidence, or map quality based on regional consistency is an important measure to evaluate the quality of robot maps. It is classically handled analyzing occupancy grids, which is an unnatural choice if the map is not represented by data points, but by line segments. We define a map-confidence measure that is tailored for segment based maps, without leaving the compact data representation by segments. The presented confidence measure is not based on comparison to ground truth data, but evaluates the map (ground truth free) based on map consistency.

[1]  Raj Madhavan,et al.  Benchmarking and standardization of intelligent robotic systems , 2009, 2009 International Conference on Advanced Robotics.

[2]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[3]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[4]  Rolf Lakaemper.  Simultaneous multi-line-segment merging for robot mapping using Mean shift clustering , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Alexander Kleiner,et al.  Real-time localization and elevation mapping within urban search and rescue scenarios: Field Reports , 2007 .

[6]  Andreas Birk,et al.  Determining Map Quality through an Image Similarity Metric , 2009, RoboCup.

[7]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge: Research Articles , 2006 .

[8]  Andreas Birk,et al.  Fast 3D mapping by matching planes extracted from range sensor point-clouds , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Arnoud Visser,et al.  Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup rescue: Field Reports , 2007 .

[10]  Rolf Lakämper,et al.  Improving sparse laser scan alignment with Virtual Scans , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Brian A. Weiss,et al.  Test arenas and performance metrics for urban search and rescue robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Arnoud Visser,et al.  Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup rescue , 2007, J. Field Robotics.

[13]  Raj Madhavan,et al.  Multi robot mapping using force field simulation: Research Articles , 2007 .