A Context Dependent Distance Measure for Shape Clustering
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Using virtual scans for improved mapping and evaluation
Autonomous Robots
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Simultaneous multi-line-segment merging for robot mapping using mean shift clustering
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Using virtual scans to improve alignment performance in robot mapping
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
A confidence measure for segment based maps
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
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This paper describes a novel approach, called Force Field Simulation, to multi robot mapping that works under the constraints given in autonomous search and rescue robotics. Extremely poor prealignment, lack of landmarks, and minimal overlap between scans are the main challenges. The presented algorithm solves the alignment problem of such laser scans utilizing a gradient descent approach motivated by physics, namely simulation of movement of masses in gravitational fields, but exchanges laws of physics with constraints given by human perception. Experiments on different real world data sets show the successful application of the algorithm. We also provide an experimental comparison with classical ICP implementation and a Lu/Milios-type alignment algorithm. © 2007 Wiley Periodicals, Inc.