Name
Abstract | ||
|---|---|---|
The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this article, we propose an alte... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TRO.2021.3050328 | IEEE Transactions on Robotics |
Keywords | DocType | Volume |
Optimization,Convergence,Sparse matrices,Simultaneous localization and mapping,Maximum likelihood estimation,Standards,Manifolds | Journal | 37 |
Issue | ISSN | Citations |
5 | 1552-3098 | 0 |
PageRank | References | Authors |
0.34 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Bai | 1 | 0 | 1.01 |
| Teresa A. Vidal-Calleja | 2 | 73 | 15.59 |
| Giorgio Grisetti | 3 | 2362 | 130.91 |