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.

Study of the motion artefacts of skin-mounted inertial sensors under different attachment conditions.

Abstract: A common problem shared by accelerometers, inertial sensors and any motion measurement method based on skin-mounted sensors is the movement of the soft tissues covering the bones. The aim of this work is to propose a method for the validation of the attachment of skin-mounted sensors. A second-order (mass-spring-damper) model was proposed to characterize the behaviour of the soft tissue between the bone and the sensor. Three sets of experiments were performed. In the first one, different procedures to excite the system were evaluated to select an adequate excitation stimulus. In the second one, the selected stimulus was applied under varying attachment conditions while the third experiment was used to test the model. The heel drop was chosen as the excitation method because it showed lower variability and could discriminate between different attachment conditions. There was, in agreement with the model, a trend to increase the natural frequency of the system with decreasing accelerometer mass. An important result is the development of a standard procedure to test the bandwidth of skin-mounted inertial sensors, such as accelerometers mounted on the skin or markers heavier than a few grams.

Redundant MEMS-IMU integrated with GPS for performance assessment in sports

Abstract: In this article, we investigate two different algorithms for the integration of GPS with redundant MEMS-IMUs. Firstly, the inertial measurements are combined in the observation space to generate a synthetic set of data which is then integrated with GPS by the standard algorithms. In the second approach, the method of strapdown navigation needs to be adapted in order to account for the redundant measurements. Both methods are evaluated in experiments where redundant MEMS-IMUs are fixed in different geometries: orthogonally-redundant and skew-redundant IMUs. For the latter configuration, the performance improvement using a synthetic IMU is shown to be 30% on the average. The extended mechanization approach provides slightly better results (about 45% improvement) as the systematic errors of the individual sensors are considered separately rather than their fusion when forming compound measurements. The maximum errors are shown to be reduced even by a factor of 2.

Self-contained positioning method and system thereof for water and land vehicles

Abstract: A positioning method and system for water and land vehicles is disclosed for highly accurate and self-contained operation. In which, an inertial navigation system (INS) is built on the micro MEMS (MicroElectroMechanicalSystem) IMU that is the core of the position determination system. To compensate the error of the INS, multiple navigation sensors are integrated into the system. The magnetic sensor is used as a magnetic field sensor to measure the heading of the vehicle. The odometer is used to measure the distance when the vehicle is on land. An automated Zero velocity updating method is used to calibrate the ever increasing INS errors. When the vehicle is in the water, a velocimeter is used to measure water speed for the INS aiding.

Sensor Fusion Applied to Autonomous Aerial Refueling

Abstract: DOI: 10.2514/1.34589 A methodology for performing sensor fusion using differential Global Positioning System receivers, inertial measurements, wireless communication, and electro-optical measurements is presented. An estimation structure basedonanextendedKalman filterisdevelopedtoestimate thepreciserelativeposition,velocity,andattitudeoftwo aircraft flying incloseproximity. Theprojectisfocusedonthedevelopmentofthismethodologyforautomaticaerial refueling in which a boom control system uses the output of the sensor fusion to guide the boom into the receiver receptacle. Results from a real-time, hardware-in-the-loop demonstration are presented which show estimator performance and feedback control capability on a scaled model.