Name
Abstract | ||
|---|---|---|
This paper presents a new approach for integrating semantic information for vision-based vehicle navigation. Although vision-based vehicle navigation systems using premapped visual landmarks are capable of achieving submeter level accuracy in large-scale urban environment, a typical error source in this type of systems comes from the presence of visual landmarks or features from temporal objects in the environment, such as cars and pedestrians. We propose a gated factor graph framework to use semantic information associated with visual features to make decisions on outlier/inlier computation from three perspectives: the feature tracking process, the geo-referenced map building process, and the navigation system using pre-mapped landmarks. The class category that the visual feature belongs to is extracted from a pre-trained deep learning network trained for semantic segmentation. The feasibility and generality of our approach is demonstrated by our implementations on top of two vision-based navigation systems. Experimental evaluations validate that the injection of semantic information associated with visual landmarks using our approach achieves substantial improvements in accuracy on GPS-denied navigation solutions for large-scale urban scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/itsc.2017.8317859 | 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) |
DocType | Volume | ISSN |
Conference | abs/1801.00858 | 2153-0009 |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Varun Murali | 1 | 11 | 2.89 |
| Han-Pang Chiu | 2 | 94 | 10.83 |
| Supun Samarasekera | 3 | 792 | 85.72 |
| Rakesh Kumar | 4 | 1923 | 157.44 |