Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a $360^\circ$ LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-WA dataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering $\sim$4km$^2$ area. Localization annotations are provided for Perth-WA. The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released.
翻译:精密本地化对于自主飞行器至关重要 。 我们展示了一种自我监督的学习方法, 首次使用变异器使用LiDAR数据进行户外本地化任务 。 我们提出一个预文本任务, 重组360 元circ$ LiDAR 的切片以利用其轴值特性 。 我们的模型叫做 Sice 变换器, 在系统处理切片时使用多头关注。 根据我们的知识, 这是利用多头关注来吸引户外点云的首例。 我们还引入了 Perth- WA 数据集, 该数据集提供了西澳大利亚珀斯市大型的LiDAR 地图, 覆盖了 $4km$2美元的区域。 为 Perth- WA 提供了本地化说明 。 拟议的本地化方法在 Perth- WA 和 Apoollo- SouthBay 数据集上得到了彻底评价。 我们还利用模型 Net40 和 ScirnN 数据集, 将公开发布我们的自我监管学习方法。 代码和 Perth- WA 数据 。