资源极度受限平台上的惯性导航:方法、机遇和挑战。

Swapnil Sayan Saha, Yayun Du, Sandeep Singh Sandha, Luis Antonio Garcia, Mohammad Khalid Jawed, Mani Srivastava
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引用次数: 0

摘要

惯性导航为在资源极为有限的物联网(IoT)平台上拒绝GPS的环境中进行定位提供了一种占地面积小、功耗低、成本低的途径。传统上,在惯性里程计中,使用特定应用的启发式和基于物理的运动学模型来减轻漂移的诅咒。这些技术虽然很轻,但无法处理域偏移和环境非线性。最近,与基于启发式的方法相比,深度神经惯性序列学习在没有人类知识的情况下捕捉非线性运动动力学方面显示出优越的里程分辨率。这些基于人工智能的技术缺乏数据,资源使用过度,无法保证遵循底层系统物理。本文强调了将实时人工智能增强惯性导航算法移植到物联网平台的独特方法、机遇和挑战。首先,我们讨论了平台感知神经架构搜索与超轻模型主干相结合如何产生31-134倍小的神经惯性里程计模型,但达到或超过最先进的人工智能增强技术的定位分辨率。该框架可以生成适合定位人类、动物、水下传感器、飞行器和精密机器人的模型。接下来,我们将展示神经符号人工智能的技术如何产生物理信息和可解释的神经惯性导航模型。之后,我们提供了在一个新领域中微调预先训练的里程计模型的机会,只需1分钟的标记数据,同时讨论了廉价的数据收集和标记技术。最后,我们确定了几个开放的研究挑战,需要在未来仔细考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.

Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.

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