Name
Title | ||
|---|---|---|
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale |
Abstract | ||
|---|---|---|
The letter presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR. The descriptors can be used for two-stage 6DoF relocalization. First, a course position is retrieved by finding candidates with the closest global descriptor in the database of geo-tagged point clouds. Then, the 6DoF pose between a query point cloud and a database point cloud is estimated by matching local descriptors and using a robust estimator such as RANSAC. Our method has a simple, fully convolutional architecture based on a sparse voxelized representation. It can efficiently extract a global descriptor and a set of keypoints with local descriptors from large point clouds with tens of thousand points. Our code and pretrained models are publicly available on the project website. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LRA.2021.3133593 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Localization,range sensing,deep learning methods | Journal | 7 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacek Komorowski | 1 | 4 | 4.13 |
| Monika Wysoczanska | 2 | 0 | 0.34 |
| Tomasz Trzcinski | 3 | 0 | 0.34 |