Name
Abstract | ||
|---|---|---|
We present a two-stage transfer learning method for training state-dependent sensor measurement models (SDSMMs) with limited sensor data. This method can alleviate collecting sizeable sensor and ground truth data to learn accurate sensor models, especially when we must learn many sensor models (for example, a fleet of autonomous cars, drones, or warehouse robots). In the first stage, we use prior ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636407 | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | DocType | ISSN |
Location awareness,Training,Atmospheric measurements,Transfer learning,Robot vision systems,Training data,Robot sensing systems | Conference | 2153-0858 |
ISBN | Citations | PageRank |
978-1-6654-1714-3 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Troi Williams | 1 | 0 | 0.34 |
| Yu Sun | 2 | 208 | 35.82 |