Name
Abstract | ||
|---|---|---|
Perception systems for outdoor robotics have to deal with varying environmental conditions. Variations in illumination in particular, are currently the biggest challenge for vision-based perception. In this paper we present an approach for radiometric characterization of multispectral cameras. To enable spatio-temporal mapping we also present a procedure for in-situ illumination estimation, resulting in radiometric calibration of the collected images. In contrast to current approaches, we present a purely data driven, parameter free approach, based on maximum likelihood estimation which can be performed entirely on the field, without requiring specialised laboratory equipment. Our routine requires three simple datasets which are easily acquired using most modern multispectral cameras. We evaluate the framework with a cost-effective snapshot multispectral camera. The results show that our method enables the creation of quatitatively accurate relative reflectance images with challenging on field calibration datasets under a variety of ambient conditions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989768 | ICRA |
Field | DocType | Volume |
Radiometric calibration,Computer vision,Data-driven,Multispectral image,Remote sensing,Maximum likelihood,Lens (optics),Artificial intelligence,Engineering,Snapshot (computer storage),Calibration,Robotics | Conference | 2017 |
Issue | Citations | PageRank |
1 | 1 | 0.37 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raghav Khanna | 1 | 30 | 5.12 |
| In-kyu Sa | 2 | 186 | 18.55 |
| Juan I. Nieto | 3 | 939 | 88.52 |
| Roland Siegwart | 4 | 7640 | 551.49 |