Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions

• URL: http://arxiv.org/abs/2412.02370v1
• Date: Tue, 03 Dec 2024 10:54:37 GMT
• Title: Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
• Authors: Eerik Alamikkotervo, Henrik Toikka, Kari Tammi, Risto Ojala,
• Abstract summary: Trajectory-based self-supervised methods can learn from traversed route without manual labels. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset.
• Score: 1.3724491757145387
• License:
• Abstract: 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

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