ZaVI - State Estimation Solely based on Prior Knowledge and Inertial Sensing

ZaVI - 仅基于先验知识和惯性传感的状态估计

• 批准号: 394554808
• 负责人: Professor Dr.-Ing. Udo Frese
• 金额: --
• 依托单位: Arbeitsgruppe Multisensorische Interaktive Systeme (agmsis)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/394554808?language=en
• 关键词: ZaVI; State; Estimation; Solely; based

Inertial Measurement Units (IMUs) allow to determine the position and orientation of a body in space. They were initially mainly used in aerospace applications but are nowadays miniaturized as a single chip and used in every smartphone or fitness tracker. An IMU is a ''relative sensor'' that measures rate of change. It consists of a 3-axes gyrometer, which measures the rotation of a body in space, and a 3-axes accelerometer that measures the acceleration of that body. The bodies' orientation can be obtained by accumulating the rotations measured by the gyrometer and the bodies' velocity and position can be obtained by accumulating the accelerations measured (including gravity). This is the canonical way to obtain the bodies' state, i.e. orientation, velocity, and position from IMU data. While accumulating the measurements, measurement errors accumulate as well, leading to a drift in the state, i.e. the state becomes more and more erroneous over time. Thus, an IMU is usually fused with a complementary ''absolute sensor'' such as GPS or camera. This is a textbook example of sensor-fusion. This project investigates, under which circumstances one can avoid error accumulation without adding a second sensor but by using prior knowledge on the type of motion and type of environment occurring. This can be considered fusion of IMU and prior knowledge. The investigation takes place on two levels:On the one hand, there exist methods from the literature that evaluate IMU data for a specific purpose without involving a second sensor. It shall be examined, how far these can be viewed as fusion with prior knowledge, which prior knowledge they exactly fuse, and whether the algorithm is equivalent to performing Bayes-estimation with the prior knowledge as a-priori distribution.The contribution here is to work out a common framework for understanding the different methods. On the other hand, circumstances that limit the movement occurring are frequent. Here, typical examples shall be investigated in how far these circumstances can be formalized and which consequences they theoretically have on the observability of orientation, velocity, and position, i.e. which of these quantities does not drift any more after being fused with prior knowledge. Further, it shall be investigatedhow this prior knowledge can be modeled as an a-priori distribution in the Bayesian sense, which fusion algorithm is suitable and how precise the result is. The examined examples come from sport science, an area that involves a large variety of movements which are interesting to measure. Examples include frequent ''wait and run'' events, where a prior on velocity shall be used as well as track cycling where the non-planar track geometry presumably even makes the position observable, and bouldering with prior knowledge about the environment.

惯性测量单元（伊穆斯）允许确定物体在空间中的位置和方向。它们最初主要用于航空航天应用，但现在被小型化为单个芯片，并用于每一款智能手机或健身追踪器。IMU是一种测量变化率的“相对传感器”。它由一个3轴陀螺仪和一个3轴加速度计组成，陀螺仪测量物体在空间中的旋转，加速度计测量物体的加速度。物体的方位可以通过累加陀螺仪测量的旋转来获得，并且物体的速度和位置可以通过累加测量的加速度（包括重力）来获得。这是从IMU数据中获得物体状态（即方向、速度和位置）的规范方法。在累积测量的同时，测量误差也累积，导致状态的漂移，即状态随着时间变得越来越错误。因此，IMU通常与互补的“绝对传感器”（如GPS或相机）融合。这是传感器融合的一个典型例子。本项目调查，在何种情况下，可以避免错误积累，而不增加第二个传感器，但通过使用先验知识的运动类型和环境发生的类型。这可以被认为是IMU和先验知识的融合。调查发生在两个层面上：一方面，存在的方法，从文献中评估IMU数据的特定目的，而不涉及第二个传感器。它应检查，在多大程度上这些可以被视为与先验知识的融合，他们确切地融合先验知识，以及该算法是否等同于执行贝叶斯估计与先验分布的先验知识。这里的贡献是制定一个共同的框架来理解不同的方法。另一方面，限制流动的情况经常发生。在这里，典型的例子将研究这些情况下可以在多大程度上被形式化，以及它们在理论上对方向，速度和位置的可观测性有哪些影响，即这些量中的哪些在与先验知识融合后不再漂移。此外，还将研究如何将这种先验知识建模为贝叶斯意义上的先验分布，哪种融合算法是合适的，以及结果的精度如何。所研究的例子来自体育科学，这是一个涉及各种各样有趣的运动来衡量的领域。示例包括频繁的“等待和运行”事件，其中应使用速度的先验，以及非平面轨道几何形状甚至可能使位置可观察的轨道自行车，以及关于环境的先验知识的抱石。

项目成果

State Observability through Prior Knowledge: Tracking Track Cyclers with Inertial Sensors

• DOI: 10.1109/ipin.2019.8911757
• 发表时间: 2019-09
• 期刊: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
• 作者: Tom L. Koller;U. Frese

Event-Domain Knowledge in Inertial Sensor Based State Estimation of Human Motion

基于惯性传感器的人体运动状态估计中的事件域知识

• DOI: 10.23919/fusion49751.2022.9841378
• 发表时间: 2022
• 期刊: 2022 25th International Conference on Information Fusion (FUSION)
• 作者: T. L. Koller;T. Laue;U. Frese
• 通讯作者: U. Frese

The Interacting Multiple Model Filter on Boxplus-Manifolds

• DOI: 10.1109/mfi49285.2020.9235232
• 发表时间: 2020-09
• 期刊: 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
• 作者: Tom L. Koller;U. Frese

State Observability through Prior Knowledge: A Conceptional Paradigm in Inertial Sensing

• DOI: 10.5220/0007952307810788
• 发表时间: 2019-07
• 作者: Tom L. Koller;Tim Laue;U. Frese