Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDAR based mapping techniques can produce robust occupancy maps, learning the parameters of such models demand considerable computational time, discouraging them from being used in real-time and large-scale applications such as autonomous driving. Recognizing the fact that real-world structures exhibit similar geometric features across a variety of urban environments, in this paper, we argue that it is redundant to learn all geometry dependent parameters from scratch. Instead, we propose a theoretical framework building upon the theory of optimal transport to adapt model parameters to account for changes in the environment, significantly amortizing the training cost. Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes. We validate various domain adaptation paradigms through a series of experiments, ranging from inter-domain feature transfer to simulation-to-real-world feature transfer. Experiments verified the possibility of estimating parameters with a negligible computational and memory cost, enabling large-scale probabilistic mapping in urban environments.

中文翻译：

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占用映射的在线域适配

创建考虑到不确定性的准确空间表示对于自主机器人在非结构化环境中安全导航至关重要。尽管最近基于 LIDAR 的映射技术可以生成稳健的占用图，但学习此类模型的参数需要大量的计算时间，这阻碍了它们在自动驾驶等实时和大规模应用中的使用。认识到现实世界结构在各种城市环境中表现出相似的几何特征这一事实，在本文中，我们认为从头开始学习所有几何相关参数是多余的。相反，我们提出了一个基于最佳运输理论的理论框架，以适应模型参数以应对环境变化，从而显着摊销培训成本。此外，通过使用高保真驾驶模拟器和真实世界的数据集，我们展示了如何自动调整 2D 和 3D 占用地图的参数以适应局部空间变化。我们通过一系列实验验证了各种域适应范式，从域间特征转移到模拟到现实世界的特征转移。实验验证了以可忽略不计的计算和内存成本估计参数的可能性，从而在城市环境中实现大规模概率映射。从域间特征转移到模拟到现实世界的特征转移。实验验证了以可忽略不计的计算和内存成本估计参数的可能性，从而在城市环境中实现大规模概率映射。从域间特征转移到模拟到现实世界的特征转移。实验验证了以可忽略不计的计算和内存成本估计参数的可能性，从而在城市环境中实现大规模概率映射。