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.

A review of visual inertial odometry from filtering and optimisation perspectives

Abstract: Visual inertial odometry (VIO) is a technique to estimate the change of a mobile platform in position and orientation overtime using the measurements from on-board cameras and IMU sensor. Recently, VIO attracts significant attentions from large number of researchers and is gaining the popularity in various potential applications due to the miniaturisation in size and low cost in price of two sensing modularities. However, it is very challenging in both of technical development and engineering implementation when accuracy, real-time performance, robustness and operation scale are taken into consideration. This survey is to report the state of the art VIO techniques from the perspectives of filtering and optimisation-based approaches, which are two dominated approaches adopted in the research area. To do so, various representations of 3D rigid motion body are illustrated. Then filtering-based approaches are reviewed, and followed by optimisation-based approaches. The links between these two approaches will ...

Calibration of the accelerometer triad of an inertial measurement unit, maximum likelihood estimation and Cramér-Rao bound

Abstract: In this paper, a simple method to calibrate the accelerometer cluster of an inertial measurement unit (IMU) is proposed. The method does not rely on using a mechanical calibration platform that rotates the IMU into different precisely controlled orientations. Although the IMU is rotated into different orientations, these orientations do not need to be known. Assuming that the IMU is stationary at each orientation, the norm of the input is considered equal to the gravity acceleration. As the orientations of the IMU are unknown, the calibration of the accelerometer cluster is stated as a blind system identification problem where only the norm of the input to the system is known. Under the assumption that the sensor noises have a white Gaussian distribution the system identification problem is solved using the maximum likelihood estimation method. The accuracy of the proposed calibration method is compared with the Cramer-Rao bound for the considered calibration problem.

Data Fusion for Driver Behaviour Analysis

Abstract: A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), an Inertial Measurement Unit (IMU) and a GPS. By fusing this information, the system can infer the behaviour of the driver, providing aggressive behaviour detection. By means of accurate GPS-based localization, the system is able to add context information, such as digital map information, speed limits, etc. Several parameters and signals are taken into account, both in the temporal and frequency domains, to provide real time behaviour detection. The system was tested in urban, interurban and highways scenarios.

Guided missile subsystem

Abstract: A guided missile subsystem including a Kalmanized radar track loop driven by acceleration signals of the missile generated by an inertial measuring unit (IMU), and a missile control loop driven by estimates of the relative kinematics of the missile and target computed by the radar track loop is disclosed. The IMU driven Kalmanized radar track loop accommodates the use of a high performance radar, like a synthetic aperture radar, for example, which operates to measure radar data at a low rate on the order of 1 Hz, to generate estimates of relative target and missile kinematics to drive the control loop at rates compatible with high performance missile kinematics. The Kalmanized track loop effects an exchange of IMU errors for "dynamic lag" errors of conventional track loops which cannot be modeled very well, and can change very rapidly. In contrast, the IMU errors can be modeled well, and in addition change very slowly which is what permits the Kalmanization function to work well in the track loop at reduced rates. Because of the dynamic exactness of the track loop, very good estimates of the relative kinematics of the missile may be supplied to the control loop to effect more accurate computations of maneuver commands which drive the controls of the missile. Moreover, the Kalmanized track loop does not let large amounts of angle glint noise into the control loop prior to missile impact. An effective bandwidth decrease as glint noise increases is provided without incurring a dynamic lag error penalty.

Method and apparatus for determining a user input from inertial sensors

Abstract: A system and method for determining a user input based on movements of a mobile device. In a particular implementation, angular movement of the mobile device about at least one axis of a linear plane is detected. Linear movement of the mobile device along at least one axis of the linear plane is also detected. Finally, a user input is determined based on the detection of the angular movement and the detection of the linear movement.