论文标题
信任，但验证：高清图更改检测的跨模式融合
Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
论文作者
论文摘要
高清（HD）地图更改检测是确定传感器数据和地图数据何时由于现实世界的变化而彼此不一致的任务。我们通过从9个月的自动驾驶汽车车队操作中挖掘数千个小时的数据来收集我们拥有信托的任务的第一个数据集，但要验证（TBV）数据集。我们提出了基于学习的公式，以解决鸟类视图和自我视图中的问题。由于实际地图的变化很少，并且向量图易于合成操纵，因此我们依靠模拟数据来训练模型。也许令人惊讶的是，我们表明这样的模型可以推广到现实世界的分布。该数据集由北美六个城市收集的地图和日志组成，是迄今为止最大的AV数据集之一，图像超过780万张图像。我们通过https://www.argoverse.org/av2.html#mapchange-link向公众提供数据，并在https://github.com/johnwlambert/tbv上使用CC BY-NC-NC-SA 4.0许可证，以及https://github.com/johnwlambert/tbv。
High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. We present learning-based formulations for solving the problem in the bird's eye view and ego-view. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train our model. Perhaps surprisingly, we show that such models can generalize to real world distributions. The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.8 million images. We make the data available to the public at https://www.argoverse.org/av2.html#mapchange-link, along with code and models at https://github.com/johnwlambert/tbv under the the CC BY-NC-SA 4.0 license.
