Name
Abstract | ||
|---|---|---|
Recent advances in real-time visual SLAM have been based primarily on mapping isolated 3-D points. This presents difficulties when seeking to ex- tend operation to wide areas, as the system state becomes large, requiring increasing computational effort. In this paper we present a novel approach to this problem in which planar structural components are embedded within the state to represent mapped points lying on a common plane. This col- lapses the state size, reducing computation and improving scalability, as well as giving a higher level scene description. Critically, the plane parameters are augmented into the SLAM state in a proper fashion, maintaining inherent uncertainties via a full covariance representation. Results for simulated data and for real-time operation demonstrate that the approach is effective. |
Year | Venue | Keywords |
|---|---|---|
2007 | BMVC | col,state space |
Field | DocType | Citations |
Computer vision,Computer science,Lying,Planar,Artificial intelligence,State space,Covariance,Computation,Scalability | Conference | 17 |
PageRank | References | Authors |
1.00 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew P. Gee | 1 | 139 | 8.80 |
| Denis Chekhlov | 2 | 164 | 10.61 |
| Walterio W. Mayol-cuevas | 3 | 497 | 48.81 |
| Andrew Calway | 4 | 645 | 54.66 |