Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915176248246272 |
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| author | Alamikkotervo, Eerik Toikka, Henrik Tammi, Kari Ojala, Risto |
| author_facet | Alamikkotervo, Eerik Toikka, Henrik Tammi, Kari Ojala, Risto |
| contents | Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging as each sample must be labeled by hand. Trajectory-based self-supervised methods offer a potential solution as they can learn from the traversed route without manual labels. However, existing trajectory-based methods use learning schemes that rely only on the camera or only on the lidar. In this paper, trajectory-based learning is implemented jointly with lidar and camera for increased performance. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset including countryside and suburb driving scenes. The source code is available at https://github.com/eerik98/lidar-camera-road-autolabeling.git |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions Alamikkotervo, Eerik Toikka, Henrik Tammi, Kari Ojala, Risto Computer Vision and Pattern Recognition Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging as each sample must be labeled by hand. Trajectory-based self-supervised methods offer a potential solution as they can learn from the traversed route without manual labels. However, existing trajectory-based methods use learning schemes that rely only on the camera or only on the lidar. In this paper, trajectory-based learning is implemented jointly with lidar and camera for increased performance. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset including countryside and suburb driving scenes. The source code is available at https://github.com/eerik98/lidar-camera-road-autolabeling.git |
| title | Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02370 |