半教師あり新規性検出によるUWB誤差信号の高精度予測
Semi-Supervised Novelty Detection for Precise Ultra-Wideband Error Signal Prediction
UWB測位の誤差を、正常環境で学習したオートエンコーダによる新規性検出で予測し、環境変化に対する信頼性を高める手法を提案した。
著者: Umberto Albertin, Alessandro Navone, Mauro Martini, Marcello Chiaberge
分類: cs.RO
原文アブストラクト
Ultra-Wideband (UWB) technology is an emerging low-cost solution for localization in a generic environment. However, UWB signal can be affected by signal reflections and non-line-of-sight (NLoS) conditions between anchors; hence, in a broader sense, the specific geometry of the environment and the disposition of obstructing elements in the map may drastically hinder the reliability of UWB for precise robot localization. This work aims to mitigate this problem by learning a map-specific characterization of the UWB quality signal with a fingerprint semi-supervised novelty detection methodology. An unsupervised autoencoder neural network is trained on nominal UWB map conditions, and then it is used to predict errors derived from the introduction of perturbing novelties in the environment. This work poses a step change in the understanding of UWB localization and its reliability in evolving environmental conditions. The resulting performance of the proposed method is proved by fine-grained experiments obtained with a visual tracking ground truth.
