Integrating Deep Semantic Segmentation into 3D Point Cloud Registration

Zaganidis, Anestis and Sun, Li and Duckett, Tom and Cielniak, Grzegorz (2018) Integrating Deep Semantic Segmentation into 3D Point Cloud Registration. Robotics and Automation Letters IEEE, 3 (4). pp. 2942-2949. ISSN 2377-3766

Full content URL: https://doi.org/10.1109/LRA.2018.2848308

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Integrating Deep Semantic Segmentation into 3D Point Cloud Registration
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Abstract

Point cloud registration is the task of aligning 3D scans of the same environment captured from different poses. When semantic information is available for the points, it can be used as a prior in the search for correspondences to improve registration. Semantic-assisted Normal Distributions Transform (SE-NDT) is a new registration algorithm that reduces the complexity of the problem by using the semantic information to partition the point cloud into a set of normal distributions, which are then registered separately. In this paper we extend the NDT registration pipeline by using PointNet, a deep neural network for segmentation and classification of point clouds, to learn and predict per-point semantic labels. We also present the Iterative Closest Point (ICP) equivalent of the algorithm, a special case of Multichannel Generalized ICP. We evaluate the performance of SE-NDT against the state of the art in point cloud registration on the publicly available classification data set Semantic3d.net. We also test the trained classifier and algorithms on dynamic scenes, using a sequence from the public dataset KITTI. The experiments demonstrate the improvement of the registration in terms of robustness, precision and speed, across a range of initial registration errors, thanks to the inclusion of semantic information.

Keywords:SLAM, scan registration, localization, NDT
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:32390
Deposited On:26 Jun 2018 12:48

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