Name
Abstract | ||
|---|---|---|
We present a novel method to localize the vehicle on an easily accessible geo-referenced satellite image based on LiDAR. We first design a neural network to extract and compare the spatial-discriminative feature maps of the satellite image patch and the LiDAR points, and obtain the probability of correspondence. Then based on the outputs of the network, a particle filter is used to obtain the probability distribution of the vehicle pose. This method can use LiDAR points and any type of odometry as input to localize the vehicle. The experimental results show that our model can generalize well on several datasets. Compared with other methods, ours is more robust in some challenging scenarios such as the occluded or shadowed area on the satellite image. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.robot.2020.103519 | Robotics and Autonomous Systems |
Keywords | DocType | Volume |
Localization,Deep learning,LiDAR,Satellite image matching | Journal | 129 |
ISSN | Citations | PageRank |
0921-8890 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengyin Fu | 1 | 814 | 60.59 |
| Minzhao Zhu | 2 | 0 | 0.34 |
| Yi Yang | 3 | 7 | 3.97 |
| Wenjie Song | 4 | 0 | 0.34 |
| meiling wang | 5 | 8 | 4.54 |