融合激光雷达与摄像头轨迹数据，实现冬季道路自动标注
Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
- 利用车行轨迹的多模态数据联合学习，无需人工标注
- 在冬日城乡复杂场景下，精度超越单一传感器方法
- 适合自动驾驶系统在恶劣天气下的道路感知优化
安全的自动驾驶与高级驾驶辅助系统需要在各种道路条件下实现鲁棒的道路分割。监督式深度学习方法在训练数据域内表现准确，但在分布外场景中不可靠。将完整数据分布纳入训练集极具挑战性，因每条样本需人工标注。基于轨迹的自监督方法提供潜在解决方案，可利用行驶路径中的数据进行学习而无需人工标签。然而，现有轨迹方法仅依赖相机或仅依赖激光雷达。本文首次将激光雷达与相机联合用于轨迹驱动的学习，显著提升性能。在包含乡郊和城区场景的严苛冬日驾驶数据集上，本方法优于近期独立使用相机或激光雷达的方法。源代码已开源：https://github.com/eerik98/lidar-camera-road-autolabeling.git
原文摘要 · Abstract (English)
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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