Name
Abstract | ||
|---|---|---|
We present an optimal method for estimat- ing the current location of a mobile robot by matching an image of the scene taken by the robot with the model of the known environment. We first derive a theoretical ac- curacy bound and then give a computational scheme that can attain that bound, which can be viewed as describ- ing the probability distribution of the current location. Using real images, we demonstrate that our method is superior to the naive least-squares method. We also con- firm the theoretical predictions of our theory by applying the bootstrap procedure. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/PL00013274 | Mach. Vis. Appl. |
Keywords | Field | DocType |
probability distribution,mobile robot,least square method | Robot localization,Mathematical optimization,Computer science,Probability distribution,Real image,Robot,Monte Carlo localization,Bootstrapping (electronics),Mobile robot,Computation | Journal |
Volume | Issue | ISSN |
13 | 2 | 0932-8092 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenichi Kanatani | 1 | 1468 | 320.07 |
| Naoya Ohta | 2 | 189 | 22.65 |