arXiv 2303.016412023-03-03cs.ROcs.AIeess.SP

RIOT：用于低成本IMU测量定位的递归惯性里程计Transformer

RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements

RMIT University（皇家墨尔本理工大学）；Victorian Department of Environment, Land, Water and Planning（维多利亚州环境、土地、水利与规划部）

James Brotchie, Wenchao Li, Andrew D. Greentree, Allison Kealy

摘要：

惯性定位是一种重要的技术，因为它可以在外部观察者不可用的条件下进行自我运动估计。然而，低成本的惯性传感器固有地受到偏差和噪声的破坏，这导致无约束的误差，使得位置的直接积分变得棘手。传统的数学方法依赖于先验系统知识、几何理论，并受到预定义动力学的约束。深度学习的最新进展受益于不断增加的数据量和计算能力，允许提供更全面理解的数据驱动解决方案。现有的深度惯性里程计解决方案依赖于估计潜在状态，例如速度，或者依赖于固定的传感器位置和周期性运动模式。在这项工作中，我们提出采取传统的状态估计递归方法，并将其应用于深度学习领域。我们的方法在训练过程中结合了真实位置先验，在惯性测量和地面真实位移数据上进行训练，允许递归并学习运动特性和系统误差偏差和漂移。我们提出了两个端到端的框架，姿态不变的深度惯性里程计，利用自我注意力来捕获惯性数据中的空间特征和长距离依赖关系。我们针对自定义的2层门控递归单元评估了我们的方法，以相同的方式对相同的数据进行了训练，并在许多不同的用户，设备和活动上测试了每种方法。每个网络都有一个序列长度加权的相对轨迹误差平均值$\leq0.4594$ m，突出了我们在模型开发中使用的学习过程的有效性。

英文摘要：

Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, that benefit from ever-increasing volumes of data and computational power, allow for data driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependant on fixed sensor positions and periodic motion patterns. In this work we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and to learn both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilise self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean $\leq0.4594$m, highlighting the effectiveness of our learning process used in the development of the models.

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。

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英文摘要：

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