Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure

Zaganidis, Anestis and Magnusson, Martin and Duckett, Tom and Cielniak, Grzegorz (2017) Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure. In: International Conference on Intelligent Robots and Systems (IROS), 24-28 Sep 2017, Vancouver, Canada.

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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.

Keywords:trajectory, Mixture of Gaussians, Gaussian distribution, Simultaneous localization and mapping, scan registration, NDT
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G790 Artificial Intelligence not elsewhere classified
Divisions:College of Science > School of Computer Science
ID Code:28481
Deposited On:01 Sep 2017 07:51

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