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Erschienen in:
Utilization of Robot Systems in Disaster Sites of the Great Eastern Japan Earthquake Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Super-Voxel Based Segmentation and Classification of 3D Urban Landscapes with Evaluation and Comparison

verfasst von : Ahmad Kamal Aijazi, Paul Checchin, Laurent Trassoudaine

Erschienen in: Field and Service Robotics

Verlag: Springer Berlin Heidelberg

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Abstract

Classification of urban range data into different object classes offers several challenges due to certain properties of the data such as density variation, inconsistencies due to holes and the large data size which requires heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from Lidar sensors is presented. The 3D point cloud is first segmented into voxels which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metrics is presented which combines both segmentation and classification results simultaneously. The proposed method is evaluated on standard datasets using three different evaluation metrics.

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Vorheriges Kapitel Large Scale Monocular Vision-Only Mapping from a Fixed-Wing sUAS
Nächstes Kapitel Classification of 3-D Point Cloud Data that Includes Line and Frame Objects on the Basis of Geometrical Features and the Pass Rate of Laser Rays
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Metadaten
Titel
Super-Voxel Based Segmentation and Classification of 3D Urban Landscapes with Evaluation and Comparison
verfasst von
Ahmad Kamal Aijazi
Paul Checchin
Laurent Trassoudaine
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Field and Service Robotics

Print ISBN: 978-3-642-40685-0

Electronic ISBN: 978-3-642-40686-7

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-40686-7_34

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