论文标题
可区分的映射网络：学习稀疏视觉本地化的结构化映射表示
Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization
论文作者
论文摘要
映射和本地化，最好是从少量观察结果中，是机器人技术中的基本任务。我们通过在新颖的神经网络体系结构中结合空间结构（可区分映射）和端到端学习来解决这些任务：可区分的映射网络（DMN）。 DMN构造了一个空间结构的视图图映射，并使用粒子过滤器将其用于随后的视觉定位。由于DMN体系结构是端到端的，因此我们可以使用梯度下降共同学习地图表示和本地化。我们将DMN应用于稀疏的视觉定位，从已知的角度来看，机器人需要相对于少数图像的新环境进行定位。我们使用模拟环境和具有挑战性的现实街道视图数据集评估了DMN。我们发现DMN学习有效的图表表示视觉定位。空间结构的好处随着较大的环境，更多的映射观点以及训练数据稀缺而增加。项目网站：http：//sites.google.com/view/differentiable-mapping
Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for mapping, and when training data is scarce. Project website: http://sites.google.com/view/differentiable-mapping
