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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
TL;DR: In this paper, the authors proposed a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional.
Abstract: We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional. The visual part of the system performs a bundle-adjustment like optimization on a sparse set of points, but unlike key-point based systems it directly minimizes a photometric error. This makes it possible for the system to track not only corners, but any pixels with large enough intensity gradients. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between keyframes. We explicitly include scale and gravity direction into our model and jointly optimize them together with other variables such as poses. As the scale is often not immediately observable using IMU data this allows us to initialize our visual-inertial system with an arbitrary scale instead of having to delay the initialization until everything is observable. We perform partial marginalization of old variables so that updates can be computed in a reasonable time. In order to keep the system consistent we propose a novel strategy which we call "dynamic marginalization". This technique allows us to use partial marginalization even in cases where the initial scale estimate is far from the optimum. We evaluate our method on the challenging EuRoC dataset, showing that VI-DSO outperforms the state of the art.

118 citations

Journal ArticleDOI
TL;DR: A novel system involving a GNSS RTK that returns a reference trajectory through the use of a suit, imbedded with inertial sensors, to reveal subject segment motion is examined, capable of measuring an entire ski course with less manpower and therefore lower cost compared with camcorder-based techniques.
Abstract: To date, camcorders have been the device of choice for 3D kinematic measurement in human locomotion, in spite of their limitations. This study examines a novel system involving a GNSS RTK that returns a reference trajectory through the use of a suit, imbedded with inertial sensors, to reveal subject segment motion. The aims were: (1) to validate the system's precision and (2) to measure an entire alpine ski race and retrieve the results shortly after measuring. For that purpose, four separate experiments were performed: (1) forced pendulum, (2) walking, (3) gate positions, and (4) skiing experiments. Segment movement validity was found to be dependent on the frequency of motion, with high accuracy (0.8°, s = 0.6°) for 10 s, which equals ∼10 slalom turns, while accuracy decreased slightly (2.1°, 3.3°, and 4.2° for 0.5, 1, and 2 Hz oscillations, respectively) during 35 s of data collection. The motion capture suit's orientation inaccuracy was mostly due to geomagnetic secular variation. The system ...

118 citations

Journal ArticleDOI
29 Jun 2011-Sensors
TL;DR: Several fusion algorithms for using multiple IMUs to enhance performance are developed and the analysis of each filter’s performance focuses on accuracy and availability, the most important characteristics of a pedestrian navigation system.
Abstract: A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.

117 citations

Patent
07 May 1996
TL;DR: In this article, an integrated GPS/inertial navigation apparatus consisting of a receiver and an inertial navigation system was used to estimate the heading of the vehicle and the displacement of each of the two antennas from the inertial sensors of the system.
Abstract: The integrated GPS/inertial navigation apparatus utilizes satellite signals received with two spatially-separated antennas to achieve improved heading estimates for a mobile platform. Each satellite signal comprises one or more component signals with each component signal having a different carrier frequency. The integrated GPS/inertial navigation apparatus consists of a receiver and an inertial navigation system. The receiver measures the carrier phase of each of one or more component signals of one or more satellite signals received by each of the two antennas during successive time periods of duration Tp. Phase measured during a Tp time period is called Tp-phase. Only one component signal of one satellite signal received by one antenna is measured during any Tp time period. The receiver utilizes the Tp-phases of each component signal obtained during a Tk time period to estimate the phase of the component signal at the end of the Tk time period, the estimated phase at the end of the Tk time period being called the Tk-phase. The inertial navigation system, comprising inertial sensors and a digital processor, utilizes the Tk-phases in determining the heading of the vehicle and the displacement of each of the two antennas from the inertial sensors of the inertial navigation system. The measured phase of a component signal is subject to error as a result of the satellite signal traversing the ionosphere. The inertial navigation system achieves more accurate estimates of heading and antenna displacements by utilizing the Tk-phases in determining ionospheric corrections to phase.

117 citations

Proceedings ArticleDOI
06 May 2013
TL;DR: This work proposes an online approach for estimating the time offset between the data obtained from different sensors in extended Kalman filter (EKF)-based methods, and demonstrates that the proposed approach yields high-precision, consistent estimates in scenarios involving both constant and time-varying offsets.
Abstract: When measurements from multiple sensors are combined for real-time motion estimation, the time instant at which each measurement was recorded must be precisely known. In practice, however, the timestamps of each sensor's measurements are typically affected by a delay, which is different for each sensor. This gives rise to a temporal misalignment (i.e., a time offset) between the sensors' data streams. In this work, we propose an online approach for estimating the time offset between the data obtained from different sensors. Specifically, we focus on the problem of motion estimation using visual and inertial sensors in extended Kalman filter (EKF)-based methods. The key idea proposed here is to explicitly include the time offset between the camera and IMU in the EKF state vector, and estimate it online along with all other variables of interest (the IMU pose, the camera-to-IMU calibration, etc). Our proposed approach is general, and can be employed in several classes of estimation problems, such as motion estimation based on mapped features, EKF-based SLAM, or visual-inertial odometry. Our simulation and experimental results demonstrate that the proposed approach yields high-precision, consistent estimates, in scenarios involving both constant and time-varying offsets.

117 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