Abstract
Place recognition is a critical capability for autonomous vehicles. It matches current sensor data with a pre-built database to provide coarse localization results. However, the effectiveness of long-term place recognition may be degraded by environment changes, such as seasonal or weather changes. To have a deep understanding of this issue, we conduct a comprehensive evaluation study on several state-of-the-art range sensing-based (i.e., LiDAR and radar) place recognition methods on the Borease dataset, which encapsulates long-term localization scenarios with stark seasonal variations and adverse weather conditions. In addition, we design a novel metric to evaluate the influence of matching thresholds on place recognition performance for long-term localization. Our results and findings provide fresh insights to the community and potential directions for future study.
Original language | English |
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Pages (from-to) | 1-12 |
Number of pages | 12 |
Journal | IEEE Transactions on Intelligent Vehicles |
DOIs | |
Publication status | Accepted/In press - 2024 |
Keywords
- Autonomous Vehicles
- Feature extraction
- Laser radar
- Location awareness
- Long-term Localization
- Measurement
- Meteorology
- Place Recognition
- Point cloud compression
- Range Sensing
- Sensors
- Urban Environments
ASJC Scopus subject areas
- Automotive Engineering
- Control and Optimization
- Artificial Intelligence