Inertial measurement unit

About: Inertial measurement unit is a(n) research topic. Over the lifetime, 13326 publication(s) have been published within this topic receiving 189083 citation(s). The topic is also known as: IMU.

Strapdown inertial navigation technology

Abstract: Inertial navigation is widely used for the guidance of aircraft, missiles ships and land vehicles, as well as in a number of novel applications such as surveying underground pipelines in drilling operations. This book discusses the physical principles of inertial navigation, the associated growth of errors and their compensation. It draws current technological developments, provides an indication of potential future trends and covers a broad range of applications. New chapters on MEMS (microelectromechanical systems) technology and inertial system applications are included.

Micromachined inertial sensors

Abstract: This paper presents a review of silicon micromachined accelerometers and gyroscopes. Following a brief introduction to their operating principles and specifications, various device structures, fabrication, technologies, device designs, packaging, and interface electronics issues, along with the present status in the commercialization of micromachined inertial sensors, are discussed. Inertial sensors have seen a steady improvement in their performance, and today, microaccelerometers can resolve accelerations in the micro-g range, while the performance of gyroscopes has improved by a factor of 10/spl times/ every two years during the past eight years. This impressive drive to higher performance, lower cost, greater functionality, higher levels of integration, and higher volume will continue as new fabrication, circuit, and packaging techniques are developed to meet the ever increasing demand for inertial sensors.

Estimation of IMU and MARG orientation using a gradient descent algorithm

Abstract: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

Pedestrian tracking with shoe-mounted inertial sensors

Abstract: A navigation system that tracks the location of a person on foot is useful for finding and rescuing firefighters or other emergency first responders, or for location-aware computing, personal navigation assistance, mobile 3D audio, and mixed or augmented reality applications. One of the main obstacles to the real-world deployment of location-sensitive wearable computing, including mixed reality (MR), is that current position-tracking technologies require an instrumented, marked, or premapped environment. At InterSense, we've developed a system called NavShoe, which uses a new approach to position tracking based on inertial sensing. Our wireless inertial sensor is small enough to easily tuck into the shoelaces, and sufficiently low power to run all day on a small battery. Although it can't be used alone for precise registration of close-range objects, in outdoor applications augmenting distant objects, a user would barely notice the NavShoe's meter-level error combined with any error in the head's assumed location relative to the foot. NavShoe can greatly reduce the database search space for computer vision, making it much simpler and more robust. The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot.

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

Abstract: One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https://github.com/HKUST-Aerial-Robotics/VINS-Mono ) and iOS mobile devices ( https://github.com/HKUST-Aerial-Robotics/VINS-Mobile ).