Name
Abstract | ||
|---|---|---|
This study addressed the problem of active localization, which requires massive computation. To solve the problem, we developed abstracted measurements that consist of qualitative metrics estimated by a single camera. These are contextual representations consisting of perceived landmarks and their spatial relations, and they can be shared by humans and robots. Next, we enhanced the Markov localization method to support contextual representations with which a robot's location can be sufficiently estimated. In contrast to passive methodologies, our approach actively uses the greedy technique to select a robot's action and improve localization results. The experiment was carried out in an indoor environment, and results indicate that the proposed active-semantic localization yields more efficient localization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICSMC.2009.5346258 | SMC |
Keywords | Field | DocType |
information gain,contextual map,active localization,active-semantic localization,proposed active-semantic localization yield,contextual representations,abstracted measurement,mobile robots,markov localization method,efficient localization,localization result,greedy algorithms,cameras,robot,indoor environment,massive computation,greedy technique,markov processes,single consumer-grade camera,qualitative metrics,contextual representation,robot kinematics,feature extraction,spatial relation | Spatial relation,Computer vision,Markov process,Computer science,Markov chain,Robot kinematics,Greedy algorithm,Feature extraction,Artificial intelligence,Robot,Mobile robot,Machine learning | Conference |
ISSN | ISBN | Citations |
1062-922X E-ISBN : 978-1-4244-2794-9 | 978-1-4244-2794-9 | 13 |
PageRank | References | Authors |
0.79 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuho Yi | 1 | 39 | 6.22 |
| Il Hong Suh | 2 | 780 | 110.60 |
| Gi Hyun Lim | 3 | 162 | 17.33 |
| Byung-Uk Choi | 4 | 50 | 14.62 |