混雑環境におけるUWB TDOA測位のための不確かさを考慮したガウス混合モデル
Uncertainty-aware Gaussian Mixture Model for UWB Time Difference of Arrival Localization in Cluttered Environments
UWBのTDOA測位において、障害物によるバイアスや非ガウスノイズに対処するため、推定状態の不確かさを組み込んだガウス混合モデルによる二段階最適化で測位とノイズモデル学習を同時に行う手法を提案し、実環境で有効性を示した。
著者: Wenda Zhao, Abhishek Goudar, Mingliang Tang, Xinyuan Qiao, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
Ultra-wideband (UWB) time difference of arrival(TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimization-based joint localization and noise model learning algorithm to address this problem. In particular, we use a Gaussian mixture model (GMM) to approximate the measurement noise distribution. We explicitly incorporate the estimated state's uncertainty into the GMM noise model learning, referred to as uncertainty-aware GMM, to improve both noise modeling and localization performance. We first evaluate the GMM noise model learning and localization performance in numerous simulation scenarios. We then demonstrate the effectiveness of our algorithm in extensive real-world experiments using two different cluttered environments. We show that our algorithm provides accurate position estimates with low-cost UWB sensors, no prior knowledge about the obstacles in the space, and a significant amount of UWB radios occluded.
