The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Preston-Krebs, Elliot Lisus, Daniil Barfoot, Timothy D. |
| author_facet | Preston-Krebs, Elliot Lisus, Daniil Barfoot, Timothy D. |
| contents | A key element of many odometry pipelines using spinning frequency-modulated continuous-wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction greatly impacts the overall performance of point-cloud-based odometry. This paper provides a first-of-its-kind, comprehensive comparison of 13 common radar point-cloud extractors for the task of iterative closest point based odometry in autonomous driving environments. Each extractor's parameters are tuned and tested on two FMCW radar datasets using approximately 176km of data from public roads. We find that the simplest, and fastest extractor, K-strongest, is the best overall extractor, consistently outperforming the average by 13.59% and 24.94% on each dataset, respectively. Additionally, we highlight the significance of tuning an extractor and the substantial improvement in odometry accuracy that it yields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry Preston-Krebs, Elliot Lisus, Daniil Barfoot, Timothy D. Robotics A key element of many odometry pipelines using spinning frequency-modulated continuous-wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction greatly impacts the overall performance of point-cloud-based odometry. This paper provides a first-of-its-kind, comprehensive comparison of 13 common radar point-cloud extractors for the task of iterative closest point based odometry in autonomous driving environments. Each extractor's parameters are tuned and tested on two FMCW radar datasets using approximately 176km of data from public roads. We find that the simplest, and fastest extractor, K-strongest, is the best overall extractor, consistently outperforming the average by 13.59% and 24.94% on each dataset, respectively. Additionally, we highlight the significance of tuning an extractor and the substantial improvement in odometry accuracy that it yields. |
| title | The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.12256 |