基于轨迹的道路自动标注：冬季条件下激光雷达与相机融合
Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
AI总结：
本文提出一种结合激光雷达与相机的轨迹自监督道路自动标注方法，在冬季驾驶数据集上优于单传感器方法，提升道路分割性能。
摘要：
在所有道路条件下进行稳健的道路分割是安全自动驾驶和高级驾驶辅助系统的必要条件。监督式深度学习方法在其训练数据领域内能够提供准确的道路分割，但在分布外场景中不可信赖。将整个分布纳入训练集具有挑战性，因为每个样本都必须手工标注。基于轨迹的自监督方法提供了一种潜在解决方案，因为它们可以在无需人工标注的情况下从已行驶路线中学习。然而，现有的基于轨迹的方法所使用的学习方案仅依赖相机或仅依赖激光雷达。本文中，轨迹学习与激光雷达和相机联合实现以提高性能。在包含乡村和郊区驾驶场景的具有挑战性的冬季驾驶数据集上评估时，我们的方法优于近期仅基于相机和仅基于激光雷达的方法。源代码可在 https://github.com/eerik98/lidar-camera-road-autolabeling.git 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
