Name
Abstract | ||
|---|---|---|
Long-term place recognition for vehicles or robots in outdoor environment is still a tackling issue: numerous changes occur in appearance due to illumination variations or weather phenomena for instance, when using visual sensors. Few methods from the literature try to manage different visual sources while it could favor data interoperability across variable sensors. In this paper, we emphasis our works on cases where there is a need to associate data from different imaging sources (optics, sensors size and even spectral ranges). We developed a method with a first camera which composes the visual memory. Afterwards, we consider another camera which partially covers the same journey. Our goal is to associate live images to the prior visual memory thanks to visual features invariant to sensors changes, with the help of a probabilistic approach for the implementation part. |
Year | Venue | Field |
|---|---|---|
2017 | European Signal Processing Conference | Computer vision,Image description,Image sensor,Computer science,Visualization,Visual memory,Robustness (computer science),Invariant (mathematics),Artificial intelligence,Probabilistic logic,Robot |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabien Bonardi | 1 | 0 | 0.34 |
| Samia Ainouz | 2 | 23 | 7.62 |
| R. Boutteau | 3 | 47 | 8.69 |
| Yohan Dupuis | 4 | 61 | 10.12 |
| Xavier Savatier | 5 | 118 | 17.42 |
| Pascal Vasseur | 6 | 267 | 28.03 |