Name
Abstract | ||
|---|---|---|
This paper addresses the localization and mapping problem for a robot moving through a (possibly) unknown environment where indistinguishable landmarks can be detected. A set theoretic approach to the problem is presented. Computationally efficient algorithms for measurement-to-feature matching, estimation of landmark positions, estimation of robot location and heading are derived, in terms of uncertainty regions, under the hypothesis that errors affecting all sensors measurements are unknown-but-bounded. The proposed technique is validated in both simulation and experimental setups. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1023/B:AURO.0000008670.09004.ce | Auton. Robots |
Keywords | Field | DocType |
localization,mapping,SLAM,uncertainty,set membership | Robot localization,Computer vision,Computer science,Artificial intelligence,Landmark,Robot,Monte Carlo localization | Journal |
Volume | Issue | ISSN |
16 | 1 | 1573-7527 |
Citations | PageRank | References |
23 | 1.42 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Di Marco | 1 | 94 | 5.89 |
| Andrea Garulli | 2 | 883 | 86.33 |
| Giannitrapani, A. | 3 | 134 | 9.36 |
| Antonio Vicino | 4 | 848 | 134.14 |