Topic
Inertial reference unit
About: Inertial reference unit is a research topic. Over the lifetime, 1306 publications have been published within this topic receiving 22068 citations. The topic is also known as: IRU.
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01 Jan 2014
TL;DR: This chapter describes the types of sensors used, followed by an overview of their use in the biomechanics community, and highlights the main groups of algorithms and the various ways in which they use the available data.
Abstract: Present-day systems for human movement analysis are not portable, have a limited capture volume and require a trained technician to analyze the data. To extend the use and benefits to non-laboratory settings the acquisition should be robust, reliable and easy to perform. Ideally, data collection and analysis would be automated to the point where no trained technicians are required. Over the last decade several inertial sensor approaches have been put forward that address most of the aforementioned limitations. Advancements in micro-electro-mechanical sensors (MEMS) and orientation estimation algorithms are boosting the use of inertial sensors in motion capture applications. These sensors currently are the most promising opportunity for non-restricted human motion analysis. In this chapter we will describe the types of sensors used, followed by an overview of their use in the biomechanics community (Sect. 16.1); provide the necessary background of basic mathematics for those that want to refresh the basics of kinematics (case studies and appendix). The limitations of traditional systems can be dealt with due to the redundant information available to obtain orientation estimates. There are several different methods to derive orientation from sensor information; we will highlight the main groups of algorithms and the various ways in which they use the available data (Sect. 16.3). The chapter furthermore contains two hands-on examples to derive orientation (case study 1) and extract joint angles (case study 2, Sect. 16.4).
11 citations
10 Sep 2015
TL;DR: Algorithmic approach to reduce the error drift in navigation information using only single motion sensor i.e. inertial measurement unit (IMU) in an unstructured environment is developed for pedestrian navigation system (PNS).
Abstract: This paper aims at developing algorithmic approach to reduce the error drift in navigation information using only single motion sensor ie inertial measurement unit (IMU) in an unstructured environment The target application is pedestrian navigation system (PNS), but these algorithms are general for other applications too involving autonomous systems A MEMS IMU is mounted on the foot of the agent in order to get benefit of known walking patterns of the wearer Algorithms are derived based on motion constraints of human walking and applied to reduce the inherent error drift in inertial navigation systems (INS) Experiments are conducted in indoor/outdoor unstructured environment to validate this algorithmic approach and shown that without using any other sensors and any other pre-conditions on the environment; we can reduce navigation errors significantly only by taking into account the motion constraints of human walking
11 citations
09 Jun 2013
TL;DR: Experimental results evidence that the inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.
Abstract: Inertial navigation systems for pedestrians are infrastructure-less and can achieve sub-meter accuracy in the short/medium period. However, when low-cost inertial measurement units (IMU) are employed for their implementation, they suffer from a slowly growing drift between the true pedestrian position and the corresponding estimated position. In this paper we illustrate a novel solution to mitigate such a drift by: a) using only accelerometer and gyroscope measurements (no magnetometers required); b) including the sensor error model parameters in the state vector of an extended Kalman filter; c) adopting a novel soft heuristic for foot stance detection and for zero-velocity updates. Experimental results evidence that our inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.
11 citations
01 Jan 2013
TL;DR: An inertial navigation system is an autonomous system that provides information about position, velocity and attitude based on the measurements by inertial sensors and applying the dead reckoning (DR) principle.
Abstract: An inertial navigation system is an autonomous system that provides information about position, velocity and attitude based on the measurements by inertial sensors and applying the dead reckoning (DR) principle. DR is the determination of the vehicle’s current position from knowledge of its previous position and the sensors measuring accelerations and angular rotations. Given specified initial conditions, one integration of acceleration provides velocity and a second integration gives position. Angular rates are processed to give the attitude of the moving platform in terms of pitch, roll and yaw, and also to transform navigation parameters from the body frame to the local-level frame.
11 citations
TL;DR: In this article, the authors present an approach for filtering, integration and calibration of a strapped-down inertial navigation system for precise determining the spacecraft attitude, which contains an electrostatic micro-accelerometer, an inertial measurement unit based on the gyro sensors, an astronomical system based on star trackers, multi-head Sun sensor and magnetic sensor, the that are fixed to the spacecraft body.
11 citations