Reliable Normal Estimation from Sparse LiDAR Point Clouds

Abstract

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.