Name
Abstract | ||
|---|---|---|
An increasing number of simultaneous localization and mapping (SLAM) systems are using appearance-based localization to improve the quality of pose estimates. However, with the growing time-spans and size of the areas we want to cover, appearance-based maps are often becoming too large to handle and are consisting of features that are not always reliable for localization purposes. This paper presents a method for selecting map features that are persistent over time and thus suited for long-term localization. Our methodology relies on a CNN classifier based on image patches and depth maps for recognizing which features are suitable for life-long matchability. Thus, the classifier not only considers the appearance of a feature but also takes into account its expected lifetime. As a result, our feature selection approach produces more compact maps with a high fraction of temporally-stable features compared to the current state-of-the-art, while rejecting unstable features that typically harm localization. Our approach is validated on indoor and outdoor datasets, that span over a period of several months. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/3DV.2016.66 | 2016 Fourth International Conference on 3D Vision (3DV) |
Keywords | Field | DocType |
localization,place recognition,feature selection,SLAM,CNN,machine learning,mapping | Computer vision,Robot vision,Feature selection,Visualization,Visual localization,Computer science,Feature extraction,Artificial intelligence,Classifier (linguistics),Simultaneous localization and mapping | Conference |
ISSN | ISBN | Citations |
2378-3826 | 978-1-5090-5408-4 | 2 |
PageRank | References | Authors |
0.36 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcin Dymczyk | 1 | 42 | 3.72 |
| Elena Stumm | 2 | 44 | 4.81 |
| Juan I. Nieto | 3 | 939 | 88.52 |
| Roland Siegwart | 4 | 7640 | 551.49 |
| Igor Gilitschenski | 5 | 78 | 13.89 |