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arXiv · 2103.03394

Point Cloud based Hierarchical Deep Odometry Estimation

Farzan Erlik Nowruzi·Dhanvin Kolhatkar·Prince Kapoor·Robert Laganiere
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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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Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Robert Laganiere. 2021-03-05. Point Cloud based Hierarchical Deep Odometry Estimation. https://arxiv.org/abs/2103.03394

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