Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.5220/0010442901120121 | PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS (VEHITS) |
Keywords | DocType | Citations |
Deep Learning, Lidar, Pointcloud, Odometry | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farzan Erlik Nowruzi | 1 | 3 | 2.42 |
| Dhanvin Kolhatkar | 2 | 0 | 0.34 |
| Prince Kapoor | 3 | 0 | 0.68 |
| Robert Laganière | 4 | 300 | 35.20 |