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.

Fusing Similarity-Based Sequence and Dead Reckoning for Indoor Positioning Without Training

Abstract: The traditional fingerprinting-based positioning approach usually requires a laborious training phase to collect the measurements in an environment, which is a challenge for applications involving large buildings In this paper, we propose a novel approach to fuse similarity-based sequence and dead reckoning to track the positions of users in wireless indoor environments The reference fingerprinting map is constructed without the need for training and is based upon the geometrical relationships of the transmitters, whose positions are known and can be obtained offline The fingerprint used for online positioning is represented by a ranked sequence of transmitters based on the measured received signal strength (RSS), which is referred to as RSS sequence in this paper The similarities between this sequence and the reference fingerprints are computed and embedded into a particle filter to locate a user The displacement estimation from inertial measurement unit is then integrated into the particle filter to track a mobile user Moreover, the proposed approach can be easily extended to include other sources of sensors In this paper, frequency modulation radio signal measurements are used as the example to fuse with Wi-Fi measurements to achieve a better tracking accuracy Extensive experiments are also conducted to evaluate the proposed approach

Nonlinear attitude and gyroscope's bias estimation for a VTOL UAV

Abstract: This paper addresses the problem of attitude and heading restitution for a VTOL UAV (Vertical Take Off and Landing Unmanned Aerial Vehicle). In a first step, we propose an observation strategy to restore the attitude starting from a measure of the orientation matrix and angular rate readings polluted by an unknown bias vector. In the second step, we detail the implementation of this algorithm in the HoverEye, a ducted fan VTOL UAV developed by Bertin Technologies, equipped with an inertial measurement unit (IMU) and magnetometers. A measured orientation matrix is calculated from the gravity and the earth's magnetic field. Then, an estimated orientation is built by integration of gyroscopic readings, and corrected by the measured ones. The proposed observer, directly designed in the special orthogonal group SO(3), is as efficient as classical Extended Kalman Filtering based observers, but easier to implement in real time. Experiments achieved on the HoverEye illustrate the concept.

Inertial/doppler-satellite navigation system

Abstract: Terrestrial navigation apparatus for a vehicle includes a system of inertial sensors generating signals representative of the position and velocity of the vehicle, a data processor, and a receiver for receiving data from a doppler satellite system including a signal of known frequency as well as signals representative of the satellite''s position. The difference between the doppler frequency shift computed from the information received from the satellite and the doppler frequency shift computed by the inertial system is modeled as an observable in a Kalman filter programmed into the data processor to generate a set of error signals representative of estimates of the errors in the position and velocity signals generated by the inertial sensors. The error estimate signals are then used to correct the errors in the inertial sensors. In one disclosed embodiment, the external, observed parameter is a discrete frequency; whereas in an alternative system, it is a frequency count.

Calibration for three dimensional motion sensor

Abstract: An electronic device can include an inertial measurement unit (IMU) operative to monitor the movement of the electronic device. The IMU used in the device can be inaccurate due to the manufacturing process used to construct the IMU and to incorporate the IMU in the electronic device. To correct the IMU output, the electronic device in which the IMU is incorporated can be placed in a testing apparatus that moves the device to known orientations. The IMU output at the known orientations can be compared to an expected true IMU output, and correction factors (e.g., sensitivity and offset matrices) can be calculated. The correction factors can be stored in the device, and applied to the IMU output to provide a true output. The testing apparatus can include a fixture placed in a gimbal movable around three axes.

Fusing Low-Cost Image and Inertial Sensors for Passive Navigation

Abstract: Navigation parameters (position, velocity, and attitude) can be estimated using optical measurements combined with an inertial navigation system. This can be accomplished by tracking stationary optical features in multiple images and using the resulting geometry to estimate and remove inertial errors. A critical factor governing the performance of image-aided inertial navigation systems is the robustness of the feature tracking algorithm. Previous research has shown the benefit of coupling the sensors at the measurement level using a tactical-grade inertial sensor. While the tactical-grade sensor is a reasonable choice for larger platforms, the greater size and cost of the sensor limits its use in smaller platforms. In this paper, an image-aided inertial navigation algorithm is implemented using a multidimensional stochastic feature tracker and low-cost sensors. The performance of the resulting navigation system is evaluated and compared. The fused image-inertial sensor is shown to outperform a free-running tactical-grade inertial sensor.