Systems and methods for determining and reducing drift in inertial navigation systems (INS). One method includes receiving images and drifted positions associated with a plurality of INS. The method includes detecting, from the plurality of images, a plurality of objects associated with the plurality of INS. The method includes determining, relative positions for the objects. The method includes generating a plurality of avatars, each having a virtual position, and associating each of the plurality of objects to one of the plurality of avatars. The method includes, for each of the INS, calculating a relative drift based on the relative position of the object and the drifted position of the INS. The method includes calculating a drift correction factor for at least one of the INS, and transmitting the drift correction factor to an electronic device associated with the INS.

Various embodiments include methods for performing temperature calibration of a first temperature sensitive unit with an electronic device having a first processing unit that is thermally coupled to the first temperature sensitive unit. Various embodiments may include determining a current temperature of the first temperature sensitive unit, determining a processing load for the first processing unit based on the current temperature and a target temperature, applying the determined processing load to the first processing unit to vary a temperature of the first temperature sensitive unit, and determining a temperature bias for the first temperature sensitive unit at the temperature of the first temperature sensitive unit based on an output of the first temperature sensitive unit.

Method, navigation device and computer program product for assisting with the navigation of a vehicle equipped with a navigation device, comprising the following steps: acquiring a priori values of kinematic variables of the navigation device, determining (202) respective current values of the kinematic variables of the navigation device and a current uncertainty matrix representative of an uncertainty of the respective current values of the kinematic variables, based on respective previous values of the kinematic variables, a previous uncertainty matrix representative of an uncertainty of the respective previous values of the kinematic variables and a model of Earth's gravity experienced by the navigation device, the modeled gravity increasing with an altitude of the navigation device.

A hybrid inertial measurement unit (IMU) comprises: a low frequency (LF) sensor providing a first signal containing information for a first parameter of the hybrid IMU; a shock resistant (SR) sensor providing a second signal containing information for the first parameter, wherein the SR sensor is resistant to destabilization during a destabilizing operational period; and a processor, wherein the processor further comprises: a weighting factor computation module to compute a weight to be applied to the first signal and to compute a weight to be applied to the second signal; a LF weighting module to apply the computed weight to the first signal to create a weighted first signal; a SR weighting module to apply the computed weight to the second signal to create a weighted second signal; and a compensator to combine the weighted first signal and the weighted second signal to create a compensated signal containing information for the first parameter.

A method and a system is provided for inertial navigation applying an Extended Kalman Filter (EKF). Upon detecting degraded quality of inertial sensor data provided by an inertial sensor device and relating to at least one inertial axis, a noise estimate used by the EKF for the inertial sensor data is temporarily increased from a first noise estimate value to a second noise estimate value. The noise estimate concerns the at least one inertial axis or the inertial sensor associated with the inertial sensor data, and the second noise estimate value is greater than the first noise estimate value. Upon detecting normalization of quality of the at least one type of inertial sensor data, the temporarily increased noise estimate value used by the EKF calculation is reset back to the first noise estimate value after a predetermined delay period after detecting said normalization of quality.

A sensor data processing system of an autonomous vehicle can receive 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 can identify an IMU data offset from a deficient IMU of the one or more IMUs, and generate an offset compensation transform to compensate for the IMU data offset from the deficient IMU. The system may then dynamically execute the offset compensation transform on the IMU data from the deficient IMU to dynamically compensate for the IMU data offset.

A system and method for collecting a set of kinematic data streams from an activity tracking system with at least one activity monitoring system that is attached to a user; segmenting at least a subset of the kinematic data streams into a set of segments according to actions of the user; generating a set of biomechanical signals from the kinematic data streams wherein a biomechanical signal characterize a biomechanical property during a segment and includes generating a first integration data set through data integration and baseline error correction across the set of segments of the normalized kinematic data stream and determining at least a first biomechanical signal that is at least partially based on the first integration data set; and applying the set of biomechanical signals.

A method for calibrating a visual-inertial tracking system is described. In one aspect, a method includes measuring a temperature of an inertial measurement unit (IMU) of a visual-inertial tracking system, identifying, from an IMU parametric model of an IMU calibration module, an IMU intrinsic parameter estimate corresponding to the temperature, determining an online IMU intrinsic parameter estimate by operating the visual-inertial tracking system with the IMU intrinsic parameter estimate, providing the online IMU intrinsic parameter estimate to the IMU calibration module, and updating and incorporating the IMU parametric model with the online IMU intrinsic parameter estimate.

Methods for improving the performance of low-cost tactical grade MEMS IMUs to reach high-end tactical grade or inertial navigation grade performance levels include exploiting advanced Deep Learning and effective stochastic models for sensor errors. The methods offer a SWaP-C alternative in a low-cost, compact weight platform compared to expensive and bulky higher grade Fiber Optic Gyroscopes and Ring Laser Gyroscopes.

Methods and systems are provided herein for measuring 3D apparatus (e.g., manual tool or tool accessory) orientation. Example implementations use an auto-nulling algorithm that incorporates a quaternion-based unscented Kalman filter. Example implementations use a miniature inertial measurement unit endowed with a tri-axis gyro and a tri-axis accelerometer. The auto-nulling algorithm serves as an in-line calibration procedure to compensate for the gyro drift, which has been verified to significantly improve the estimation accuracy in three-dimensions, especially in the heading estimation.