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Authors: Farzan Erlik Nowruzi 1 ; Dhanvin Kolhatkar 1 ; Prince Kapoor 2 and Robert Laganiere 1 ; 2

Affiliations: 1 School of Electrical Engineering and Computer Sciences, University of Ottawa, Canada ; 2 Sensorcortek Inc., Canada

Keyword(s): Deep Learning, Lidar, Pointcloud, Odometry.

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.

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-513-5; ISSN 2184-495X, SciTePress, pages 112-121. DOI: 10.5220/0010442901120121

@conference{vehits21,
author={Farzan Erlik 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 - VEHITS},
year={2021},
pages={112-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010442901120121},
isbn={978-989-758-513-5},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Point Cloud based Hierarchical Deep Odometry Estimation
SN - 978-989-758-513-5
IS - 2184-495X
AU - Nowruzi, F.
AU - Kolhatkar, D.
AU - Kapoor, P.
AU - Laganiere, R.
PY - 2021
SP - 112
EP - 121
DO - 10.5220/0010442901120121
PB - SciTePress

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