Name
Abstract | ||
|---|---|---|
A novel probabilistic online learning framework for autonomous off-road robot navigation is proposed. The system is purely vision-based and is particularly designed for predicting traversability in unknown or rapidly changing environments. It uses self-supervised learning to quickly adapt to novel terrains after processing a small number of frames, and it can recognize terrain elements such as paths, man-made structures, and natural obstacles at ranges up to 30 meters. The system is developed on the LAGR mobile robot platform and the performance is evaluated using multiple metrics, including ground truth. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IROS.2007.4399622 | 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9 |
Keywords | Field | DocType |
ground truth,learning artificial intelligence,mobile robot,supervised learning,machine learning,feature extraction,path planning,image resolution,mobile robots | Motion planning,Small number,Online learning,Computer vision,Computer science,Terrain,Ground truth,Artificial intelligence,Probabilistic logic,Robot,Mobile robot | Conference |
Citations | PageRank | References |
3 | 0.41 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ayse erkan | 1 | 105 | 12.75 |
| R. Hadsell | 2 | 1678 | 100.80 |
| Pierre Sermanet | 3 | 1788 | 185.17 |
| Jan Ben | 4 | 115 | 20.89 |
| Urs Muller | 5 | 389 | 24.17 |
| Yann LeCun | 6 | 26090 | 3771.21 |