# RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence

### Dhvani Katkoria, Jaya Sreevalsan-Nair

#### 2022

#### Abstract

Road surface geometry provides information about navigable space in autonomous driving. Ground plane estimation is done on “road” points after semantic segmentation of three-dimensional (3D) automotive LiDAR point clouds as a precursor to this geometry extraction. However, the actual geometry extraction is less explored, as it is expensive to use all “road” points for mesh generation. Thus, we propose a coarser surface approximation using road edge points. The geometry extraction for the entire sequence of a trajectory provides the complete road geometry, from the point of view of the ego-vehicle. Thus, we propose an automated system, RoSELS (Road Surface Extraction for LiDAR point cloud Sequence). Our novel approach involves ground point detection and road geometry classification, i.e. frame classification, for determining the road edge points. We use appropriate supervised and pre-trained transfer learning models, along with computational geometry algorithms to implement the workflow. Our results on SemanticKITTI show that our extracted road surface for the sequence is qualitatively and quantitatively close to the reference trajectory.

Download#### Paper Citation

#### in Harvard Style

Katkoria D. and Sreevalsan-Nair J. (2022). **RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence**. In *Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,* ISBN 978-989-758-584-5, pages 55-67. DOI: 10.5220/0011301700003277

#### in Bibtex Style

@conference{delta22,

author={Dhvani Katkoria and Jaya Sreevalsan-Nair},

title={RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence},

booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},

year={2022},

pages={55-67},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0011301700003277},

isbn={978-989-758-584-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,

TI - RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence

SN - 978-989-758-584-5

AU - Katkoria D.

AU - Sreevalsan-Nair J.

PY - 2022

SP - 55

EP - 67

DO - 10.5220/0011301700003277