能動的パーティクルフィルタネットワーク：連続行動空間と大規模地図における効率的な能動的自己位置推定
Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps
微分可能なパーティクルフィルタと強化学習を組み合わせ、地図の局所情報のみを用いて連続行動空間で能動的に自己位置推定を行う手法を提案した論文。
著者: Daniel Honerkamp, Suresh Guttikonda, Abhinav Valada
分類: cs.RO
原文アブストラクト
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.
