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Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.


Papers
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Proceedings ArticleDOI
21 Jun 1998
TL;DR: A family of algorithms for low-cost strapdown inertial navigation system for land vehicles that reduce navigation errors in the presence of relatively high instrument noise, and at the same reduce number of required inertial sensors.
Abstract: This paper develops a family of algorithms for low-cost strapdown inertial navigation system for land vehicles. Constraints on the motion of land vehicles are defined. They include constraints on vehicle's orientation relative to the Earth surface, and relationship between vehicle's attitude and its velocity direction. Navigation equations are derived that assume validity of these constraints on the vehicle's motion. Compared to standard strapdown inertial navigation, these algorithms reduce navigation errors in the presence of relatively high instrument noise, and at the same reduce number of required inertial sensors. Navigation errors are analyzed and used in an error model for the Kalman filter. Derived algorithms are applied to processing of experimental data.

57 citations

Patent
10 Sep 1982
TL;DR: In this paper, an attitude sensing system which incorporates the precision in attitude sensing of a satellite navigation system with the dynamic tracking capabilities of an inertial measurement unit is presented, where an optimal estimator filter is used to produce periodically updated attitude and system error signals.
Abstract: An attitude sensing system which incorporates the precision in attitude sensing of a satellite navigation system with the dynamic tracking capabilities of an inertial measurement unit. Optimally estimated attitude and system error signals are combined with the outputs of an inertial measurement unit to control the phase differences of signals received on an interferometer array from a navigation satellite. The outputs from the inertial measurement unit and the phase differences are processed in an optimal estimator filter to produce periodically updated attitude and system error signals. This permits inertial attitude sensing to benefit from the long term accuracy of attitude estimation based upon signals from the satellite navigation system. During conditions of high dynamic maneuvering in which attitude changes are rapid, the output of the inertial measurement unit continues to provide attitude information and controls the attitude sensing loop to insure that the system is not disoriented by such high dynamic conditions.

57 citations

Proceedings ArticleDOI
06 May 2013
TL;DR: This paper studies the observability of the nonlinear IMU-RGBD calibration system and proves that the calibration parameters are observable given observations to a single point feature and designs a consistency-improved, observability constrained (OC) extended Kalman filter (EKF)-based estimator.
Abstract: In this paper, we address the problem of extrinsically calibrating an inertial measurement unit (IMU) with respect to an RGBD sensor. In particular, we study the observability of the nonlinear IMU-RGBD calibration system and prove that the calibration parameters are observable given observations to a single point feature. Moreover, we show that the system has four unobservable directions corresponding to the global translation and rotations about the gravity vector. Based on the results of the observability analysis, we design a consistency-improved, observability constrained (OC) extended Kalman filter (EKF)-based estimator for calibrating the sensor pair while at the same time tracking its pose and creating a 3D map of the environment. Finally, we validate the key findings of the observability analysis and assess the performance of the OC-EKF estimator both in simulation and experimentally.

57 citations

Journal ArticleDOI
TL;DR: This research investigated the use of a compact stereocamera mounted on a remote-controlled helicopter to acquire stereoimages of a maize field and represent the crop information in a 3D crop map.

57 citations

Journal ArticleDOI
Abstract: There has been an increasing demand for infrastructureless localization. Current approaches involving inertial measurement unit (IMU) generally utilize step detection and step counting to estimate the displacement. However, the accuracy is affected, because the step sizes are neglected. Some groups have proposed algorithms that involve placing the IMU on the foot to estimate the step size, but users have commented that it affects their walking. Hence, this paper presents a new method to estimate both the forward displacement and orientation with the IMU placed at the upper torso. Placing IMU at the upper torso to estimate horizontal displacement has been challenging, as the accuracy of the inertial sensors is greatly handicapped by the notorious integration drift when performing integration in the travel direction with the lack of opportunity for zero velocity update. Thus, a novel method is proposed in this paper by exploiting the vertical component of the accelerometer reading. An inverted pendulum model is proposed with a step detector and a step length estimation method. The system is implemented, and two sets of experiments are conducted to demonstrate the capability. The experiment sets include straight lines and rectangular shape path, and in each set, four step sizes of small, normal, large, and mixture are conducted for each test and each test is performed four times. The experimental results show an average displacement error of 1% for straight line paths and 2% for the rectangular paths.

57 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,067
20222,256
2021852
20201,150
20191,181
20181,162