Name
Abstract | ||
|---|---|---|
We propose a weighted least-squares (WLS) algorithm for optimal pose estimation of mobile robots using geometrical maps as environment models. Pose estimation is achieved from feature correspondences in a nonlinear framework without linearization. The proposed WLS approach fields optimal estimates in the least-squares sense, is applicable to heterogencous geometrical features decomposed in points and lines, and has an O(N) computation time. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/70.988978 | IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION |
Keywords | Field | DocType |
heterogeneous features, nonlinear optimization, optimal 2-D pose estimation, weighted least-squares | Motion planning,Computer vision,3D pose estimation,Kalman filter,Pose,Artificial intelligence,Mobile robot,Linearization,Mathematics,Computational complexity theory,Computation | Journal |
Volume | Issue | ISSN |
18 | 1 | 1042-296X |
Citations | PageRank | References |
16 | 0.87 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geovany Araujo Borges | 1 | 154 | 12.82 |
| Marie-José Aldon | 2 | 147 | 11.61 |