Semantic-Assisted 3D Normal Distributions Transform for Scan Registration in Environments with Limited Structure

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Anestis Zaganidis, Martin Magnusson, Tom Duckett, and Grzegorz Cielniak
Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure
to appear in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) 2017

 

Abstract

Point cloud registration is a core problem of many robotic applications, including simultaneous localization and mapping. The Normal Distributions Transform (NDT) is a method that fits a number of Gaussian distributions to the data points, and then uses this transform as an approximation of the real data, registering a relatively small number of distributions as opposed to the full point cloud. This approach contributes to NDT’s registration robustness and speed but leaves room for improvement in environments of limited structure.

To address this limitation we propose a method for the introduction of semantic information extracted from the point clouds into the registration process. The paper presents a large scale experimental evaluation of the algorithm against NDT on two publicly available benchmark data sets. For the purpose of this test a measure of smoothness is used for the semantic partitioning of the point clouds. The results indicate that the proposed method improves the accuracy, robustness and speed of NDT registration, especially in unstructured environments, making NDT suitable for a wider range of applications.

 

@INPROCEEDINGS{zaganidis2017registration, 
 author={Anestis Zaganidis and Martin Magnusson and Tom Duckett and Grzegorz Cielniak}, 
 booktitle={Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)}, 
 title={Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure}, 
 year={2017}, 
 pages={}, 
 month=sep,
}