3D Perception and Environment Map Generation for Humanoid Robot Navigation
International Journal of Robotics Research
Categorizing Perceptions of Indoor Rooms Using 3D Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
A novel hierarchical technique for range segmentation of large building exteriors
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Robotics and Autonomous Systems
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
Robotics and Autonomous Systems
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The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended Expectation Maximization (EM) algorithm. This algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge framework. Determining the fitting quality of the gained patches, the approach then allows for segmentation of planar surfaces out of the 3D environment. The result is a set of 2D objects, which can be used as input for classical computer vision applications, in particular for object recognition. Our approach makes it possible to apply classical tools of 2D image processing to solve problems of 3D robot mapping, e.g. landmark recognition.