Reliable Normal Estimation from Sparse LiDAR Point Clouds

In this paper, we present a reliable vertex normal estimation method from sparse point clouds that improves the accuracy of plane-based frame-to-frame registration. We define a face normal reliability measure. The vertex normals are calculated by weighted averaging adjacent face normals based on the...

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Bibliographic Details
Published in2020 IEEE International Conference on Consumer Electronics (ICCE) pp. 1 - 2
Main Authors Sa, Jae-Min, Bae, Seong-Jun, Choi, Ye-Chan, Choi, Kang-Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2020
Subjects
Conferences
Consumer electronics
Estimation
Faces
Laser radar
Point cloud compression
Reliability
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Summary:In this paper, we present a reliable vertex normal estimation method from sparse point clouds that improves the accuracy of plane-based frame-to-frame registration. We define a face normal reliability measure. The vertex normals are calculated by weighted averaging adjacent face normals based on the reliability. Through the experiments, it is confirmed that the proposed method produces consistent and reliable vertex normals.
ISSN:2158-4001
DOI:10.1109/ICCE46568.2020.9043024