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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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Journal ArticleDOI
TL;DR: A quadrotor that performs autonomous navigation in complex indoor and outdoor environments and the exploration of a coal mine with obstacle avoidance and 3D mapping is presented.
Abstract: Micro air vehicles have become very popular in recent years. Autonomous navigation of such systems plays an important role in many industrial applications as well as in search-and-rescue scenarios. We present a quadrotor that performs autonomous navigation in complex indoor and outdoor environments. An operator selects target positions in the onboard map and the system autonomously plans an obstacle-free path and flies to these locations. An onboard stereo camera and inertial measurement unit are the only sensors. The system is independent of external navigation aids such as GPS. No assumptions are made about the structure of the unknown environment. All navigation tasks are implemented onboard the system. A wireless connection is only used for sending images and a three-dimensional 3D map to the operator and to receive target locations. We discuss the hardware and software setup of the system in detail. Highlights of the implementation are the field-programmable-gate-array-based dense stereo matching of 0.5 Mpixel images at a rate of 14.6 Hz using semiglobal matching, locally drift-free visual odometry with key frames, and sensor data fusion with compensation of measurement delays of 220i¾?ms. We show the robustness of the approach in simulations and experiments with ground truth. We present the results of a complex, autonomous indoor/outdoor flight and the exploration of a coal mine with obstacle avoidance and 3D mapping.

110 citations

Journal ArticleDOI
TL;DR: In this paper, a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU) is presented.
Abstract: This paper presents a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction, while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic, and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose-tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center of mass. The robustness and accuracy of the relative navigation algorithm is demonstrated through a hardware-in-the loop simulation setting, in which actual vision data for the relative navigation are generated by a laser range finder scanning a spacecraft mockup attached to a robotic motion simulator.

110 citations

24 Sep 2004
TL;DR: Test results are presented in this paper showing the performance of the integrated MEMS GPS/INS navigation system when used to perform guidance for a small Unmanned Ground Vehicle (UGV).
Abstract: This paper describes the design, operation and performance test results of a miniature, low cost integrated GPS/inertial navigation system (INS) designed for use in UAV or UGV guidance systems. The system integrates a miniaturized commercial GPS with a low grade Micro-Electro-Mechanical (MEMS) inertial measurement unit (IMU). The MEMS IMU is a small self-contained package (< 1 cu inch) and includes a triad of accelerometers and gyroscopes with additional sensors integrated for temperature compensation and baro pressure altitude aiding. The raw IMU data is provided through a serial interface to a processor board where the inertial navigation solution and integrated GPS/inertial Kalman filter is generated. The GPS/Inertial software integration is performed using NAVSYS’ modular InterNav software product. This allows integration with different low cost GPS chips sets or receivers and also allows the integrated GPS/inertial navigation solution to be embedded as an application on a customer’s host computer. This modular, object oriented architecture facilitates integration of the miniature MEMS GPS/INS navigation system for embedded navigation applications. Test results are presented in this paper showing the performance of the integrated MEMS GPS/INS navigation system when used to perform guidance for a small Unmanned Ground Vehicle (UGV). Data is provided showing the position, velocity and attitude accuracy when operating with GPS aiding and also for periods where GPS dropouts occur and alternative navigation update sources are used to bound the MEMS inertial navigation error growth.

110 citations

Journal ArticleDOI
TL;DR: A position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation and achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation.
Abstract: Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.

109 citations

Proceedings ArticleDOI
01 Oct 2012
TL;DR: Novel methods for joint axis estimation and joint position estimation are presented that exploit the kinematic constraints induced by these two types of joints.
Abstract: We consider 6d inertial measurement units (IMU) attached to rigid bodies, e.g. human limb segments or links of a robotic manipulator, which are connected by hinge joints and spheroidal joints. Novel methods for joint axis estimation and joint position estimation are presented that exploit the kinematic constraints induced by these two types of joints. The presented methods do not require any knowledge about the sensor units' positions or orientations and do not include integration, i.e. they are insensitive to measurement bias. By means of a three-links simulation model, the estimation algorithms are validated and convergence is analyzed. Finally, the algorithms are tested using experimental data from IMU-based human gait analysis.

108 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