Katrīna Tupule, Maksis Celitāns, Kaspars Ozols. Ground Segmentation Method Based on Polar Grid for 3D LiDAR Point Cloud. 2025 22nd International Conference on Networking, Sensing, and Control (ICNSC), 2025.
Bibtex citāts:
Bibtex citāts:
@inproceedings{19751_2025,
author = {Katrīna Tupule and Maksis Celitāns and Kaspars Ozols},
title = {Ground Segmentation Method Based on Polar Grid for 3D LiDAR Point Cloud},
journal = {2025 22nd International Conference on Networking, Sensing, and Control (ICNSC)},
year = {2025}
}
author = {Katrīna Tupule and Maksis Celitāns and Kaspars Ozols},
title = {Ground Segmentation Method Based on Polar Grid for 3D LiDAR Point Cloud},
journal = {2025 22nd International Conference on Networking, Sensing, and Control (ICNSC)},
year = {2025}
}
Anotācija: In autonomous driving platforms, one of the key tasks is the perception of the environment. Light Detection and Ranging (LiDAR) sensors are widely used to obtain a point cloud representation of the surrounding environment. Raw point clouds contain measurement points from different objects in the environment, and distinguishing and semantically segmenting them can be challenging, but beneficial for better understanding the environment. One of the challenges of preprocessing raw point clouds is their segmentation into groups based on the type of object they originated from. In this paper, we propose a real-time, CPU-based ground segmentation method that structures the point cloud into a polar grid and estimates the ground points based on the local features of the grid cells. Our ground segmentation method is evaluated on the SemanticKITTI dataset and achieves high accuracy with real-time performance. Index Terms—ground segmentation, autonomous driving, LiDAR, point clouds, perception, environment model.