Abstract
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. Localization is then conducted through an efficient convolutional matching between the embeddings. Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments. Our experiments illustrate the performance of the proposed approach over a large-scale dataset consisting of over 4000km of driving.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 605-616 |
| Number of pages | 12 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 87 |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 2nd Conference on Robot Learning, CoRL 2018 - Zurich, Switzerland Duration: Oct 29 2018 → Oct 31 2018 |
Keywords
- Deep Learning
- Localization
- Map-based Localization
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability