Name
Abstract | ||
|---|---|---|
This paper presents a new hybrid metric map representation (HYMM) that combines feature maps with other dense metric sensory information. The global feature map is partitioned into a set of connected Local Trian- gular Regions (LTRs), which provide a reference for a detailed multi-dimensional description of the environment. The HYMM framework permits the combination of efficient feature-based SLAM algorithms for localisation with, for example, occupancy grid (OG) maps. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. The representation presented here will allow the robot to perform DenseSLAM. DenseSLAM is the process of performing SLAM whilst obtaining a dense environment representation. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ROBOT.2004.1307181 | Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference |
Keywords | Field | DocType |
mobile robots,path planning,robot vision,self-organising feature maps,dense metric sensory information,detailed multi-dimensional description,global feature map,hybrid metric map representation,local triangular regions,metric maps,mobile robots,occupancy grid maps | Motion planning,Computer vision,Metric map,Quasi-open map,Data association,Artificial intelligence,Engineering,Robot,Mobile robot,Grid,Occupancy grid mapping | Conference |
Volume | ISSN | ISBN |
1 | 1050-4729 | 0-7803-8232-3 |
Citations | PageRank | References |
15 | 1.03 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan I. Nieto | 1 | 939 | 88.52 |
| José E. Guivant | 2 | 751 | 103.39 |
| Eduardo Mario Nebot | 3 | 171 | 16.84 |