A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Line and Rectangle Detection by Clustering and Grouping
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Using Extended EM to Segment Planar Structures in 3D
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Multi robot mapping using force field simulation: Research Articles
Journal of Field Robotics
A confidence measure for segment based maps
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Robotics and Autonomous Systems
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Line segment based representation of 2D robot maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It significantly reduces the size of the data set. It also contains higher geometric information, which is necessary for robust post processing. The paper describes an algorithm to convert global 2D robot maps to line segment representation, using a pre-aligned set of point-based single scans as input. Mean-shift clustering on the set of all line segments is utilized to merge perceptually similar segments to single instances: locally linear features in the environment are unambiguously represented by single line segments in the final global map. Apart from a scaling parameter, the approach is parameter free. Experiments on real world data sets prove its applicability in the field of robot mapping.