Topic
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.
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TL;DR: Size effect and other IMU errors are calibrated through optimal estimation with navigation errors in triaxis RINS and the navigation performance of RINS could be further improved in both the short term and long term with the proposed self-calibration method.
Abstract: Rotational inertial navigation system (RINS) could improve navigation performance by rotating the inertial measurement unit (IMU) with gimbals, and error parameters could be self-calibrated as well. However, accelerometers size effect would cause more influence on navigation accuracy in RINS than a strapdown inertial navigation system because of gimbals rotation, which should be calibrated and compensated for high-end application. In this paper, size effect and other IMU errors are calibrated through optimal estimation with navigation errors in triaxis RINS. The rotation scheme is designed by the characteristics of size effect and other errors, and all errors are verified to be observable by piece-wise constant system method and singular value decomposition method. The self-calibration method is tested by simulations and experiments. The calibration repeatability of size-effect parameters is less than 0.15 cm, while other IMU errors reach higher calibration accuracy compared with traditional methods. Navigation experiments indicate that the sharply changing velocity errors are greatly corrected after compensation, and position and velocity accuracy have improved 30% and 70%, respectively, during 12-h navigation experiment. Therefore, the navigation performance of RINS could be further improved in both the short term and long term with the proposed self-calibration method.
50 citations
TL;DR: This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs, and a spoofing Detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed.
Abstract: Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated.
50 citations
Posted Content•
TL;DR: In this paper, an online approach for calibrating temporal offset between visual and inertial measurements is proposed, which achieves temporal offset calibration by jointly optimizing time offset, camera and IMU states, as well as feature locations in a SLAM system.
Abstract: Accurate state estimation is a fundamental module for various intelligent applications, such as robot navigation, autonomous driving, virtual and augmented reality. Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years. Time instants at which different sensors' measurements are recorded are of crucial importance to the system's robustness and accuracy. In practice, timestamps of each sensor typically suffer from triggering and transmission delays, leading to temporal misalignment (time offsets) among different sensors. Such temporal offset dramatically influences the performance of sensor fusion. To this end, we propose an online approach for calibrating temporal offset between visual and inertial measurements. Our approach achieves temporal offset calibration by jointly optimizing time offset, camera and IMU states, as well as feature locations in a SLAM system. Furthermore, the approach is a general model, which can be easily employed in several feature-based optimization frameworks. Simulation and experimental results demonstrate the high accuracy of our calibration approach even compared with other state-of-art offline tools. The VIO comparison against other methods proves that the online temporal calibration significantly benefits visual-inertial systems. The source code of temporal calibration is integrated into our public project, VINS-Mono.
50 citations
28 Sep 2007
TL;DR: The trade-offs are discussed, previous work is summarized, some preliminary results on adopting different step length estimation models for the different motion regimes are presented and a comparison of two systems is compared.
Abstract: Man motion is one of the most challenging applications for a navigation system. A common approach to integrated pedestrian navigation (IPN) is to integrate GNSS user equipment and inertial sensors with magnetometers and a barometric altimeter. However, there are a number of different approaches to the use of the inertial sensors, which may be characterised by • The number and quality of inertial sensors to be used; • Whether to mount them on the shoes or the body; • Whether to use conventional inertial navigation algorithms, supported by zero velocity updates (ZVUs), pedestrian dead reckoning (PDR) or both. This paper discusses the trade-offs, summarises previous work and then compares the performance of two systems. IPN system A uses a body-mounted IMU with inertial navigation, ZVUs and PDR. IPN system B uses a shoemounted IMU with inertial navigation and ZVUs during the stance phase of every stride. Both systems successfully bridged a 60 s GPS outage with a position error of less than 10 m. The system B solution exhibited less drift during the outage, but was also noisier. System A was also used to successfully demonstrate PDR during running and jogging motion, though performance was not as good as during walking. Some preliminary results on adopting different step length estimation models for the different motion regimes are then presented.
50 citations
TL;DR: In this paper, an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed.
Abstract: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.
50 citations