Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910298307297280 |
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| author | Shami, Mamoona Birkhez Kiss, Gabriel Haakonsen, Trond Arve Lindseth, Frank |
| author_facet | Shami, Mamoona Birkhez Kiss, Gabriel Haakonsen, Trond Arve Lindseth, Frank |
| contents | Geolocation is integral to the seamless functioning of autonomous vehicles and advanced traffic monitoring infrastructures. This paper introduces a methodology to geolocate road objects using a monocular camera, leveraging the NVIDIA DriveWorks platform. We use the Centimeter Positioning Service (CPOS) and the inverse Haversine formula to geo-locate road objects accurately. The real-time algorithm processing capability of the NVIDIA DriveWorks platform enables instantaneous object recognition and spatial localization for Advanced Driver Assistance Systems (ADAS) and autonomous driving platforms. We present a measurement pipeline suitable for autonomous driving (AD) platforms and provide detailed guidelines for calibrating cameras using NVIDIA DriveWorks. Experiments were carried out to validate the accuracy of the proposed method for geolocating targets in both controlled and dynamic settings. We show that our approach can locate targets with less than 1m error when the AD platform is stationary and less than 4m error at higher speeds (i.e. up to 60km/h) within a 15m radius. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07582 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks Shami, Mamoona Birkhez Kiss, Gabriel Haakonsen, Trond Arve Lindseth, Frank Robotics Computer Vision and Pattern Recognition Geolocation is integral to the seamless functioning of autonomous vehicles and advanced traffic monitoring infrastructures. This paper introduces a methodology to geolocate road objects using a monocular camera, leveraging the NVIDIA DriveWorks platform. We use the Centimeter Positioning Service (CPOS) and the inverse Haversine formula to geo-locate road objects accurately. The real-time algorithm processing capability of the NVIDIA DriveWorks platform enables instantaneous object recognition and spatial localization for Advanced Driver Assistance Systems (ADAS) and autonomous driving platforms. We present a measurement pipeline suitable for autonomous driving (AD) platforms and provide detailed guidelines for calibrating cameras using NVIDIA DriveWorks. Experiments were carried out to validate the accuracy of the proposed method for geolocating targets in both controlled and dynamic settings. We show that our approach can locate targets with less than 1m error when the AD platform is stationary and less than 4m error at higher speeds (i.e. up to 60km/h) within a 15m radius. |
| title | Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07582 |