Name
Abstract | ||
|---|---|---|
Abstract—Most,simultaneous,localization and,mapping (SLAM) approaches,focus on purely metrical approaches,to map-building. We present a method,for computing,the global metrical,map,that builds on,the structure provided by a topological map. This allows us to factor the uncertainty,in the map,into local metrical uncertainty (which is handled,well by existing SLAM methods), global topological uncertainty (which is handled,well by recently developed,topological maplearning methods), and global metrical uncertainty (which can be handled,effectively once,the other types of uncertainty are factored,out). We believe that this method,for building the global metrical map will be,scalable,to very,large environments. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IROS.2004.1389613 | IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference |
Keywords | Field | DocType |
Markov processes,mobile robots,Markov localization,global metrical uncertainty,global topological uncertainty,scalable global metrical map-building,simultaneous localization and mapping method,topological map,topological skeleton | Computer vision,Markov process,Computer science,Topological skeleton,Artificial intelligence,Topological map,Simultaneous localization and mapping,Mobile robot,Scalability | Conference |
Volume | ISBN | Citations |
2 | 0-7803-8463-6 | 24 |
PageRank | References | Authors |
1.87 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Modayil | 1 | 403 | 29.02 |
| Patrick Beeson | 2 | 177 | 12.66 |
| Benjamin Kuipers | 3 | 4111 | 875.19 |