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• W3133867020 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." @default.
• W3133867020 created "2021-03-15" @default.
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• W3133867020 date "2021-03-05" @default.
• W3133867020 modified "2023-09-23" @default.
• W3133867020 title "Point Cloud based Hierarchical Deep Odometry Estimation." @default.
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• W3133867020 hasPublicationYear "2021" @default.
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