Point Cloud based Hierarchical Deep Odometry Estimation

• URL: http://arxiv.org/abs/2103.03394v1
• Date: Fri, 5 Mar 2021 00:17:58 GMT
• Title: Point Cloud based Hierarchical Deep Odometry Estimation
• Authors: Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Robert Laganiere
• Abstract summary: 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.
• Score: 3.058685580689605
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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