ニューラル測距慣性オドメトリ
Neural Ranging Inertial Odometry
UWB測距と慣性センサをグラフ注意ネットワークと再帰型ニューラルネットで融合し、アンカー配置やマルチパスに頑健な自己位置推定を実現した研究。
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著者: Si Wang, Bingqi Shen, Fei Wang, Yanjun Cao, Rong Xiong, Yue Wang
分類: cs.RO
原文アブストラクト
Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenarios due to its sensitivity to sensor arrangement and non-Gaussian pattern induced by multi-path or multi-signal interference, which commonly occurs in many typical applications like long tunnels. We introduce a novel neural fusion framework for ranging inertial odometry which involves a graph attention UWB network and a recurrent neural inertial network. Our graph net learns scene-relevant ranging patterns and adapts to any number of anchors or tags, realizing accurate positioning without calibration. Additionally, the integration of least squares and the incorporation of nominal frame enhance overall performance and scalability. The effectiveness and robustness of our methods are validated through extensive experiments on both public and self-collected datasets, spanning indoor, outdoor, and tunnel environments. The results demonstrate the superiority of our proposed IR-ULSG in handling challenging conditions, including scenarios outside the convex envelope and cases where only a single anchor is available.
