Name
Title | ||
|---|---|---|
Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics? |
Abstract | ||
|---|---|---|
Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1904.07967 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mubariz Zaffar | 1 | 10 | 2.85 |
| Ahmad Khaliq | 2 | 1 | 1.02 |
| Shoaib Ehsan | 3 | 110 | 24.43 |
| Michael Milford | 4 | 1221 | 84.09 |
| Kostas Alexis | 5 | 26 | 5.82 |
| Klaus D. McDonald-Maier | 6 | 327 | 54.43 |