Name
Abstract | ||
|---|---|---|
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors. We present a simple yet efficient deep network architecture to compute a rotationally invariant discriminative global descriptor from a radar scan image. The performance and generalization ability of the proposed method is experimentally evaluated on two large scale driving datasets: MulRan and Oxford Radar RobotCar. Additionally, we present a comparative evaluation of radar-based and LiDAR-based localization using learned global descriptors. Our code and trained models are publicly available on the project website. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-92270-2_39 | ICONIP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacek Komorowski | 1 | 4 | 4.13 |
| Monika Wysoczanska | 2 | 0 | 0.34 |
| Tomasz Trzcinski | 3 | 517 | 24.18 |