Described are techniques and systems for secure camera based IMU calibration for stationary systems, including vehicles. Existing vehicle camera systems are employed, with enhanced security to prevent malicious attempts by hackers to try and cause a vehicle to enter IMU calibration mode. IMU calibration occurs when a calibration system determines the vehicle is parked in a controlled environment; calibration targets are positioned at different viewing angles to vehicle cameras to act as sources of optical patterns of encoded data. Features of the patterns are for security as well as for alignment functionality. Images of the calibration targets enable inference of a vehicle coordinate system, from which calculations for IMU mounting error compensations are performed. A relative rotation between the IMU and the vehicle coordinate system are applied to IMU data to compensate for relative rotations between the vehicle and the IMU, thereby improving vehicle slope and bank metrics.

System for finding at least one mobile trolley in a locale, the system including at least one communication beacon which has a range covering the locale and which is connected to a computer control unit, and at least one electronic module mounted on the trolley and including a transmission device arranged to transmit position data to the communication beacon, and an inertial motion detection hub that includes a device for detecting linear motion along axes of a detection reference system and a device for detecting angular motion about the axes of the detection reference system and that is arranged to provide position data on the basis of linear motion measurement data and angular motion measurement data, the module being mounted on an element of the trolley such that any movement of the trolley within the locale causes angular movement of the element, the system being arranged to detect when the trolley is stopped when the angular motion measurement data correspond to zero angular motion at one measurement instant and being arranged to set to zero speeds calculated on the basis of the linear motion measurement data corresponding to the same measurement instant.

An inertial sensor includes: a plurality of inertial force detection elements each configured to output an output signal corresponding to a detected inertial force; and a processor configured to execute processing relating to the output signal from each of the plurality of inertial force detection elements. The plurality of inertial force detection elements includes: a plurality of main inertial force detection elements configured to detect inertial forces of a plurality of first predetermined axes orthogonal to each other; and a sub-inertial force detection element configured to detect an inertial force of a second predetermined axis which intersects the plurality of first predetermined axes such that the second predetermined axis is orthogonal to none of the plurality of first predetermined axes.

An integrated navigation method for a mobile vehicle is provided, which includes: acquiring a motion measurement of the mobile vehicle by using an inertial navigation element in the mobile vehicle and calculating a gesture parameter of the mobile vehicle based on the motion parameter; estimating, based on the gesture parameter, a motion state of the mobile vehicle in a real time manner by using a satellite navigation element in the mobile vehicle to obtain an error estimation value of the motion state, and correcting a motion parameter of the mobile vehicle based on the error estimation value of the motion state; and controlling an operation route of the mobile vehicle based on corrected navigation information.

A monitoring system for a speed determination system in an aircraft is disclosed. A method, which may be implemented by a computer program, of monitoring a speed determination system for an aircraft is also disclosed. The speed determination system includes a first speed measurement system and a second speed measurement system and the second speed measurement system is associated with a predetermined behaviour characteristic. The monitoring system includes a processor arranged to receive speed data provided by the speed determination system and to perform a correspondence determination process comprising processing the received speed data to determine whether a correspondence condition is satisfied, the correspondence condition having a correspondence between the received speed data and the predetermined behaviour characteristic of the second speed measurement system. In response to determining that the correspondence condition is satisfied, determine that the first speed measurement system is in an error condition.

A method for stabilizing a magnetic field of an inertial measurement unit (IMU), is provided that includes initializing accelerometer and gyroscope (AG) heading data for the IMU and initializing accelerometer, gyroscope and magnetometer (AGM) heading data for the IMU. Determining whether a tracking state exists and completing processing of the AG heading data and the AGM heading data if the tracking state does not exist. Calculating a magnetic field error if the tracking state exists.

Presented herein are systems and methods using inertial measurement units (IMUs) for providing location and guidance, and more particularly for providing location and guidance in environments where global position systems (GPS) are unavailable or unreliable (GPS denied and/or degraded environments), and for such location and guidance being provided to projectiles, munitions, or rounds that are released, fired, or deployed from vehicles or weapons systems. More particularly, this disclosure relates to the use of a series of low-accuracy or low-resolution IMUs, in combination, to provide high-accuracy or high-resolution location and guidance results. This further relates to an electronics-control system for handing off control of the measurement and guidance of a body in flight between groups or subgroups of IMUs to alternate between high dynamic range/lower resolution and lower dynamic range/higher resolution measurement and guidance as the environment dictates.

This measurement device for measuring the angular velocity or acceleration of a two-wheel vehicle, is provided with a main detection unit which detects the three-axis angular velocity or three-axis acceleration, a support unit which can support the main detection unit on the body of the two-wheel vehicle, and a correction unit which cancels the lean of the body to the left and right in the main detection unit.

A sensor data processing system for an autonomous vehicle receives inertial measurement unit (IMU) data from one or more IMUs of the autonomous vehicle. Based at least in part on the IMU data, the system identifies an IMU data offset from a deficient IMU of the one or more IMUs, and generates an offset compensation transform to compensate for the IMU data offset from the deficient IMU. The system dynamically executes the offset compensation transform on the IMU data from the deficient IMU to dynamically compensate for the IMU data offset.

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.