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.

Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter

Abstract: The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for intelligent human-robot collaboration. This paper evaluates an inertial motion capture system which registers full-body movements of an user in a robotic manipulator workplace. However, the presence of errors in the global translational measurements returned by this system has led to the need of using another localization system, based on Ultra-WideBand (UWB) technology. A Kalman filter fusion algorithm which combines the measurements of these systems is developed. This algorithm unifies the advantages of both technologies: high data rates from the motion capture system and global translational precision from the UWB localization system. The developed hybrid system not only tracks the movements of all limbs of the user as previous motion capture systems, but is also able to position precisely the user in the environment.

FlightTracker: A Novel Optical/Inertial Tracker for Cockpit Enhanced Vision

Abstract: One of the earliest fielded augmented reality applications was enhanced vision for pilots, in which a display projected on the pilot's visor provides geo-spatially registered information to help the pilot navigate, avoid obstacles, maintain situational awareness in reduced visibility, and interact with avionics instruments without looking down. This requires exceptionally robust and accurate head-tracking, for which there is not a sufficient solution yet available. In this paper, we apply miniature MEMS sensors to cockpit helmet-tracking for enhanced/synthetic vision by implementing algorithms for differential inertial tracking between helmet-mounted and aircraft-mounted inertial sensors, and novel optical drift correction techniques. By fusing low-rate inside-out and outside-in optical measurements with high-rate inertial data, we achieve millimeter position accuracy and milliradian angular accuracy, low-latency and high robustness using small and inexpensive sensors.

Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation

Abstract: To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS This system provides a 2-D navigation solution, which is adequate for land vehicles The traditional technique for this multisensor integration problem is Kalman filtering (KF) Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions An enhanced version of PF is utilized in this paper and is called the Mixture PF Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift The experimental results show that the two proposed solutions outperform all the compared counterparts

Attitude measurement using a single GPS receiver with two closely-spaced antennas

Abstract: A system determines three-dimensional attitude of a moving platform using signals from two closely spaced Global Positioning System (GPS) antennas. The system includes three rate gyroscopes and three accelerometers rigidly mounted in a fixed relationship to the platform to aid in determining the attitude. The system applies signals from a first of the two GPS antennas to sufficient channels of a GPS receiver to support navigation. The system applies signals from a second of the two GPS antennas to the remaining receive channels, which are configured to support interferometry. The system optimally selects the navigation and interferometry channels to provide an interferometric heading solution. The system resolves the ambiguity normally associated with the interferometric heading solution by having the closely spaced GPS antennas and using interferometry to refine a coarse heading estimate from a GPS plus Inertial Measurement Unit (IMU) transfer alignment solution. The system achieves close sub-meter spacing of the two GPS antennas by merging many temporal interferometric measurements that result from an attitude memory provided by the IMU time-history solution.

Semi-autonomous flying robot for physical interaction with environment

Abstract: This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.