A method of determining whether parametric performance of an inertial sensor has been degraded comprises: recording first data output from an inertial sensor; then recording second data output from the inertial sensor; comparing the first data output with the second data output; and determining whether the parametric performance of the inertial sensor has been degraded based on the comparison between the first and second data output.

A method and apparatus are provided for determining the orientation of an object relative to fixed, moving or moveable platform likely to be exposed to buffeting or similar effects. The object may for example be a helmet worn by a pilot of an aircraft in which orientation of the helmet relative the aircraft while in flight may usefully be known, in particular when determining the position of space-stabilised symbols being displayed in an associated helmet-mounted digital display system. According to the method, not only may orientation of an object may be predicted at some time ahead of a time point of validity of source sensor dataa useful feature for digital display systemsbut the functionality carrying out the prediction may be dynamically configured to according to a detected severity of buffeting upon the object and upon the tracker system sensors to reduce the effects of the buffeting upon the quality of data output by the tracker system. The method includes functionality to measure the severity of any buffeting using the same source data as used to determine orientation of the object.

Described is a system for estimating a trajectory of a borehole. The system processes signals of sensor streams obtained from an inertial sensor system. Using the set of processed signals, the system determines whether a drill is in a survey mode state or a continuous mode state, and a measured depth of the borehole is determined. A set of survey mode positioning algorithms is applied when the drill is stationary. A set of continuous mode navigation algorithms is applied when the drill is non-stationary. Using at least one Kalman filter, results of the set of survey mode positioning algorithms and the set of continuous mode navigation algorithms are combined. An estimate of a borehole trajectory and corresponding ellipse of uncertainty (EOU) is generated using the combined results.

In an embodiment, a method includes: obtaining a first signal from a first sensor of a rotorcraft, the first signal indicating measured angular velocity around a first axis of the rotorcraft; filtering the first signal with a lag compensator to estimate angular position around the first axis of the rotorcraft; and adjusting flight control devices of the rotorcraft according to the estimated angular position and the measured angular velocity around the first axis of the rotorcraft, thereby changing flight characteristics of the rotorcraft around the first axis of the rotorcraft.

A position detection device is a position detection device installed in an apparatus, and detecting a position of the apparatus, and includes an acceleration sensor configured to detect acceleration information of the apparatus, a static acceleration removing unit configured to remove static acceleration information indicating acceleration information in a static state of the apparatus, from the acceleration information detected by the acceleration sensor, and generate movement acceleration information indicating acceleration information in a moving state of the apparatus, an error deriving unit configured to derive an error component contained in the movement acceleration information, an acceleration correcting unit configured to correct the movement acceleration information by subtracting the error component from the movement acceleration information, and a position information acquiring unit configured to acquire position information of the apparatus based on the corrected movement acceleration information.

A system for navigation comprising a computing machine having a plurality of computing cores, a plurality of digital processing cores, a plurality of memory and a plurality of Magnetic and Inertial measurement Units is described. Also described are methods for navigation comprising receiving by a computing machine having a plurality of computing cores, accelerometer, gyroscope, and magnetometer data of a plurality of Magnetic and Inertial measurement Units (MIMUs) positioned to form a sensor array and fusing the data using a Madgwick filter, a Mahony filter, a Kalman filter and/or an extended Kalman filter to obtain an orientation value.

A sensor calibration device acquires a measured value of an attitude of a vehicle based on an output of an attitude sensor, acquires vehicle speed information indicating a traveling speed of the vehicle, acquires map information on a road on which the vehicle travels, and sets a calibration value applied to the measured value to cause a calculated position of the vehicle calculated based on the vehicle speed information and the measured value to come close to a reference position indicated in the map information.

An offset correction apparatus for a gyro sensor includes an acceleration sensor, a geomagnetic sensor, a stationary determination unit that determines, by using an output value of the acceleration sensor and an output value of the geomagnetic sensor, whether the gyro sensor is stationary, a difference calculation unit that calculates an offset value of the gyro sensor by using an output value of the gyro sensor, and an offset-value update unit that assumes, as a new offset value, an offset value calculated by the difference calculation unit by using an output value of the gyro sensor determined to be stationary by the stationary determination unit.

A method for generating a sensor output from the outputs of first and second sensors is provided. The method comprising receiving the outputs from the first and second sensors; estimating an offset between the outputs of the first and second sensors over a first range of outputs; adjusting the output of the second sensor based on the estimated offset; and generating a sensor output, based on the output of the first sensor, the adjusted output of the second sensor and a blending function that blends the output of the first sensor and the adjusted output of the second sensor.

Conventional techniques involve fusion of Inertial Measurement Units (IMU) sensor based method and vision based localization technique for localization of rotor systems. However vision based localization technique may be prone to errors due to motion blur, drastic lighting change, sudden rotation at UAV, and the like, while the drift in IMU based localization severely impact overall solution. Embodiments of the present disclosure provide systems and methods to eliminate (or filter) drift for dynamics model based localization of multirotors. The dynamics equations require drag modelling, which is dependent on velocity, to generate vehicles' acceleration along the body axis. The present disclosure considers the drag contribution, at velocity level, as a low frequency component. Incorrect or nonmodelling of this low frequency component leads to drift at velocity level. This drift can then be removed through a high pass filter to obtain drift free velocity data for pose estimation and better localization thereof.