Name
Abstract | ||
|---|---|---|
Our mobile robot system uses scale-invariant visual landmarks to localize itself and build a 3D map of the environment simultaneously. As image features are not noise-free, we carry out error analysis and use Kalman filters to track the 3D landmarks, resulting in a database map with landmark positional uncertainty. By matching a set of landmarks as a whole, our robot can localize itself globally based on the database containing landmarks of sufficient distinctiveness. Experiments show that recognition of position within a map without any prior estimate can be achieved using the scale-invariant landmarks |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/IROS.2001.973392 | IROS |
Keywords | Field | DocType |
distance measurement,kalman filters,3d landmarks,position recognition,scale-invariant visual landmarks,error analysis,global localization,mobile robots,motion estimation,path planning,landmark positional uncertainty,stereo image processing,local localization,hough transforms,3d map,mobile robot,kalman filter,computer science,image features,scale invariance,image analysis | Motion planning,Computer vision,Feature (computer vision),Computer science,Kalman filter,Visual landmarks,Artificial intelligence,Motion estimation,Robot,Landmark,Mobile robot | Conference |
Volume | ISBN | Citations |
1 | 0-7803-6612-3 | 56 |
PageRank | References | Authors |
6.16 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Se | 1 | 786 | 92.81 |
| D. G. Lowe | 2 | 15718 | 1413.60 |
| James J. Little | 3 | 2430 | 269.59 |