Particle Filter Networks with Application to Visual Localization
Particle Filter Networks with Application to Visual Localization
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发表时间：
2018-05
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通讯作者：
Peter Karkus;David Hsu;Wee Sun Lee
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作者：
Peter Karkus;David Hsu;Wee Sun Lee
粒子滤波是一种强大的序列状态估计方法，在机器人定位、目标跟踪等领域有着广泛的应用。要将粒子滤波应用于实际，一个关键的挑战是建立概率系统模型，特别是对于具有复杂动力学或丰富感官输入的系统，如摄像机图像。介绍了粒子滤波网络(PFnet)，它将系统模型和粒子滤波算法编码在单个神经网络中。PF-Net是完全可区分的，并根据数据进行端到端的训练。它不是学习一般的系统模型，而是学习针对粒子过滤器算法优化的模型。我们将PF-Net应用于视觉定位任务，其中机器人必须在丰富的3-D世界中定位，仅使用示意性的2-D平面图。在仿真实验中，在不同的传感器输入下，PF-Net的性能一致优于其他学习结构以及传统的基于模型的方法。此外，PF-Net很好地适用于新的、看不见的环境。
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or rich sensory inputs such as camera images. This paper introduces the Particle Filter Network (PFnet), which encodes both a system model and a particle filter algorithm in a single neural network. The PF-net is fully differentiable and trained end-to-end from data. Instead of learning a generic system model, it learns a model optimized for the particle filter algorithm. We apply the PF-net to a visual localization task, in which a robot must localize in a rich 3-D world, using only a schematic 2-D floor map. In simulation experiments, PF-net consistently outperforms alternative learning architectures, as well as a traditional model-based method, under a variety of sensor inputs. Further, PF-net generalizes well to new, unseen environments.