神经测距惯性里程计
Neural Ranging Inertial Odometry
AI总结：
本文提出一种基于神经融合的测距惯性里程计框架，通过图注意力网络和递归神经网络提升定位精度和鲁棒性，适用于复杂环境。
摘要：
超宽频（UWB）由于其轻量级和无漂移特性，在GPS拒止定位中展现出巨大潜力。然而，其在实际场景中的精度受限于传感器布置和多径或多信号干扰引起的非高斯模式。我们提出了一种新的神经融合框架，用于测距惯性里程计，包括图注意力UWB网络和递归神经惯性网络。我们的图网络学习场景相关的测距模式，并适应任意数量的锚点或标签，实现无校准的精确定位。此外，最小二乘法的整合和名义框架的引入增强了整体性能和可扩展性。通过在公共和自采集数据集上的广泛实验，验证了我们方法的有效性和鲁棒性，涵盖了室内外和隧道环境。结果表明，我们提出的IR-ULSG在处理具有挑战性的条件时表现出色，包括凸包外的场景和仅有一个锚点可用的情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
