Accurate localization is a critical requirement for most robotic tasks. The main body of existing work is focused on passive localization in which the motions of the robot are assumed given, abstracting from their influence on sampling informative observations. While recent work has shown the benefits of learning motions to disambiguate the robot's poses, these methods are restricted to granular discrete actions and directly depend on the size of the global map. We propose Active Particle Filter Networks (APFN), an approach that only relies on local information for both the likelihood evaluation as well as the decision making. To do so, we couple differentiable particle filters with a reinforcement learning agent that attends to the most relevant parts of the map. The resulting approach inherits the computational benefits of particle filters and can directly act in continuous action spaces while remaining fully differentiable and thereby end-to-end optimizable as well as agnostic to the input modality. We demonstrate the benefits of our approach with extensive experiments in photorealistic indoor environments built from real-world 3D scanned apartments. Videos and code are available at http://apfn.cs.uni-freiburg.de.

翻译：精确的本地化是大多数机器人任务的关键要求。 现有工作的主要部分侧重于被动本地化,假设机器人的动作,从机器人对抽样信息观测的影响中摘取其作用。 尽管最近的工作显示学习移动来分解机器人的外形的好处,但这些方法仅限于颗粒离散动作,直接取决于全球地图的大小。 我们提议了活性粒子过滤网络(APFN),这种方法仅依赖本地信息进行可能性评估和决策。 为了做到这一点,我们将不同的粒子过滤器与一个覆盖地图最相关部分的强化学习剂结合起来。由此产生的方法继承了粒子过滤器的计算效益,可以直接在连续的行动空间采取行动,同时保持完全不同,从而最终至终端的可选性,以及输入模式的微异性。我们展示了我们的方法的好处,在现实世界3D扫描公寓建造的光现实室内环境中进行广泛的实验。 视频和代码可以在http://apfn.c. uni-fredeburg.