Range-View-Based Normal Estimation for Sparse LiDAR SLAM.

Accurate surface normal estimation is critical for LiDAR-based tasks such as mapping, localization, and scene understanding. However, traditional 3D neighborhood selection methods often struggle with sparse point clouds due to uneven density and sensor limitations. In this paper, we propose a geometry-aware approach that leverages the structured 2D range view to select more consistent neighborhoods for normal estimation. By combining adaptive windowing in range space with distance-based filtering, our method provides robust and accurate normal vectors across both dense and sparse regions. We integrate this method into a lightweight SLAM framework and evaluate it using indoor LiDAR data from a Velodyne-16 sensor. The results demonstrate improved normal estimation and enhanced localization and mapping performance, highlighting the method's effectiveness for indoor robotic applications.