Name
Abstract | ||
|---|---|---|
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-030-28619-4_38 | ISRR |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Leivas Oliveira | 1 | 225 | 9.70 |
| Noha Radwan | 2 | 3 | 1.03 |
| W Burgard | 3 | 14438 | 1393.44 |
| Thomas Brox | 4 | 7866 | 327.52 |