Name
Abstract | ||
|---|---|---|
Dense image alignment works by minimizing the photometric error of two images since it is assumed that the illumination changes between images close in time remain the same-this is what is called the brightness constancy assumption. However, this assumption does not hold with long-term maps since illumination changes continually from day to day (morning, afternoon, evening) and is dependent on certain external conditions like weather or even seasons. In this work, we present an image registration algorithm based on the Normalized Information Distance (NID) that is shown to be robust to extreme illumination changes comparing to the traditional direct methods. The pose is estimated by minimizing the NID function with the help of the nonlinear least square optimization library G2O. We share our source code1 (CPU and GPU version) for the benefit of the community, which can be a strong basis for future tracking and mapping system based on NID. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636532 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaozhong Chen | 1 | 0 | 0.68 |
| Christoffer R. Heckman | 2 | 12 | 10.78 |