ベイズフィルタのための深層計測更新
Deep Measurement Updates for Bayes Filters
深層学習による条件付きエンコーダ・デコーダで深度画像から観測確率を計算し、ベイズフィルタの計測更新を汎用的に置き換える手法を提案。合成データのみで学習しながら実世界データでも良好に動作し、物体姿勢推定から関節系の状態推定まで検証した。
著者: Johannes Pankert, Maria Vittoria Minniti, Lorenz Wellhausen, Marco Hutter
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, we propose the novel approach Deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has a conditional encoder-decoder neural network structure to process depth images as raw inputs. Even though the network is trained only on synthetic data, the model shows good performance at evaluation time on real-world data. With our proposed training scheme primed data training , we demonstrate how the DMU models can be trained efficiently to be sensitive to condition variables without having to rely on a stochastic information bottleneck. We validate the proposed methods in multiple scenarios of increasing complexity, beginning with the pose estimation of a single object to the joint estimation of the pose and the internal state of an articulated system. Moreover, we provide a benchmark against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a baseline comparison for articulation state estimation.
