Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Recent Advances in Range Image Segmentation
Selected Papers from the International Workshop on Sensor Based Intelligent Robots
Results on Range Image Segmentation for Service Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Comparison of surface normal estimation methods for range sensing applications
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Comparison of surface normal estimation methods for range sensing applications
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Online semantic mapping of urban environments
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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In mobile robotics, the segmentation of range data is an important prerequisite to object recognition and environment understanding. This paper presents an algorithm for realtime segmentation of a continuous stream of incoming range data. The method is an extension of the previously developed RBNN algorithm and proceeds in two phases: Firstly, the normal vector of each incoming point is estimated from its neighborhood, which is continuously monitored. Secondly, new points are clustered according to their Euclidean and angular distance to previously clustered points. An outline of the algorithm complexity as well as the parameters that influence the segmentation performance is provided. Three benchmark scenarios in which the algorithm is deployed on a mobile robot with a laser range finder confirm that the method can robustly segment incoming data at high rates.