Point Cloud based Hierarchical Deep Odometry Estimation

Farzan Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Robert Laganiere, Robert Laganiere

2021

Abstract

Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art.

Paper Citation

in Harvard Style

Nowruzi F., Kolhatkar D., Kapoor P. and Laganiere R. (2021). Point Cloud based Hierarchical Deep Odometry Estimation. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 112-121. DOI: 10.5220/0010442901120121

in Bibtex Style

@conference{vehits21,
author={Farzan Nowruzi and Dhanvin Kolhatkar and Prince Kapoor and Robert Laganiere},
title={Point Cloud based Hierarchical Deep Odometry Estimation},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={112-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010442901120121},
isbn={978-989-758-513-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Point Cloud based Hierarchical Deep Odometry Estimation
SN - 978-989-758-513-5
AU - Nowruzi F.
AU - Kolhatkar D.
AU - Kapoor P.
AU - Laganiere R.
PY - 2021
SP - 112
EP - 121
DO - 10.5220/0010442901120121