Aerial vehicle sensor calibration systems and methods are provided herein. An example method includes determining a jarring event, determining when a drone is level relative to an aerial vehicle platform of a vehicle, the vehicle having a calibration controller, determining when the vehicle is level relative to a subordinate surface, and transmitting a signal to a drone controller by the calibration controller to calibrate a gyroscope or an accelerometer of the drone.

Disclosed are a system for drone calibration related to calibration that is required prior to flying a drone, and a method therefor. According to the present invention, there is an effect of improving the convenience of a calibration operation required for flying a drone, and in addition, when multiple drones have to be flying at the same time, there is an effect of allowing the drown to be easily calibrated without manually calibrating each of the multiple drones.

A method for determining a current estimated gyroscope bias of a gyroscope, the gyroscope being configured to output rotation rate data, the method comprising: receiving first rotation rate data associated with a first time from the gyroscope, the first rotation rate data comprising a first rotation rate reading that indicates a rotation rate of the gyroscope about a first axis; calculating a rotation rate moving average associated with the first time based on the first rotation rate data and a rotation rate moving average associated with a second time earlier than the first time; calculating a moving standard deviation associated with the first time based on the first rotation rate data, the rotation rate moving average associated with the first time, and a moving standard deviation associated with the second time; determining if the moving standard deviation associated with the first time is less a threshold moving standard deviation; and in response the moving standard deviation being less than the threshold moving standard deviation, using the first rotation rate reading to update the current estimated gyroscope bias.

A wearable computing device, including a device body configured to be affixed to a body of a user. The wearable computing device may further include an inertial measurement unit (IMU) and a processor. The processor may receive kinematic data from the IMU while the device body is affixed to the body of the user. The processor may perform a first coordinate transformation on the kinematic data into a training coordinate frame of a training wearable computing device. At a first machine learning model trained using training data including training kinematic data collected at the training wearable computing device, the processor may compute a training-frame velocity estimate for the wearable computing device based on the transformed kinematic data. The processor may perform a second coordinate transformation on the training-frame velocity estimate to obtain a runtime-frame velocity estimate and may output the runtime-frame velocity estimate to a target program.

Inertial measurement units (IMUs) and methods with adaptations to eliminate or minimize sensor error, offset, or bias shift. More particularly, such IMUs and methods for gun-fired projectiles and particularly adapted to accurately measure forces and to prevent or minimize the error, offset, or bias shift associated with events exhibiting high g shock, and/or high levels of vibration, and/or rotation. Even more particularly, such IMUs and methods utilizing novel IMU packaging adapted to prevent or minimize sensor error, offset, or bias shift, and recalibration adaptations and methods adapted to correct or reset the error, offset, or bias shift from such an event. Ultimately relates to IMUs that are adapted to provide accurate measurements prior to, during and after such event, and to provide continuous accurate measurements during flight of gun-fired projectiles.

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

An inertial sensor based surgical navigation system for knee replacement surgery is disclosed. Inertial sensors composed of six-degree-of-freedom inertial chips, whose measurements are processed through a series of integration, quaternion, and kalman filter algorithms, are used to track the position and orientation of bones and surgical instruments. The system registers anatomically significant geometry, calculates joint centers and the mechanical axis of the knee, develops a visualization of the lower extremity that moves in real time, assists in the intra-operative planning of surgical cuts, determines the optimal cutting planes for cut guides and the optimal prosthesis position and orientation, and finally navigates the cut guides and the prosthesis to their optimal positions and orientations using a graphical user interface.

The present disclosure relates to methods of enhancing a navigation solution about a device and a platform, wherein the mobility of the device may be constrained or unconstrained within the platform, and wherein the navigation solution is provided even in the absence of normal navigational information updates (such as, for example, GNSS). More specifically, the present method comprises utilizing measurements from sensors (e.g. accelerometers, gyroscopes, magnetometers etc.) within the device to calculate and resolve the attitude of the device and the platform, and the attitude misalignment between the device and the platform.

The presently disclosed subject matter includes a method and system directed for calculating azimuth of an airborne platform during flight based on IMU measurements, without using GNSS data, gyrocompassing or magnetometers operating on the ground for determining the azimuth.

A surveying instrument comprises a base; an alidade rotatable about a first axis relative to the base; and an optical measuring instrument having a measuring axis rotatable about a second axis relative to the alidade. A beam path can be provided for a light beam using components including a light source, lenses, mirrors, beam splitters, and a position-sensitive detector. The surveying can be calibrated by performing plural measurements at different orientations of the alidade relative to the base and different orientations of the measuring instrument relative to the alidade using the above components.