A bias estimation device includes a determination unit configured to determine whether a forklift is stationary based on a measurement value from a first sensor configured to detect information regarding movement of the forklift and a measurement value from a second sensor configured to detect information regarding a cargo handling device included in the forklift, an accumulation unit configured to accumulate an IMU measurement value that is a measurement value from an inertial measurement unit included in the forklift when the determination unit determines that the forklift is stationary and stop accumulation of the IMU measurement value when the determination unit determines that the forklift is not stationary, and a calculation unit configured to calculate a bias of the IMU measurement value based on the IMU measurement value accumulated in the accumulation unit.

An inertial measurement unit (IMU) self-test system includes an IMU and a control circuit. The control circuit is configured to receive IMU data collected by the IMU and inputs from systems external to the IMU indicative of mechanical stimulus, wherein the control circuit utilizes IMU data collected in response to the mechanical stimulus to determine IMU validity.

A method, apparatus and computer program product provide for error mitigation in an inertial navigation system (INS) based upon centripetal acceleration experienced by a vehicle that carries the INS. In the context of a method, the centripetal acceleration experienced by a vehicle carrying the INS that is making a turn is determined. The method also includes determining a velocity of the vehicle in reliance upon the centripetal acceleration. Based on the velocity determined in reliance upon the centripetal acceleration, the method further includes updating one or more filters of the INS to correct a velocity estimate provided by the INS.

This disclosure relates to an inertial sensor, and to the field of embedded designs. The inertial sensor includes at least one inertial measurement unit; a controller configured to read measurement data of the at least one inertial measurement unit, wherein the at least one inertial measurement unit is couplable to or decouplable from the controller; and a plurality of sets of interfaces, wherein each set of interfaces has a first end electrically connected with the controller and a second end electrically connected with one of the at least one inertial measurement unit.

In an embodiment of the disclosed principles, a strap down integration (SDI) system includes a gyroscope that provides gyroscope data samples and an accelerometer that provides accelerometer data samples. A timing capture module associates a timestamp in a common time-base to data samples, and an SDI module linked to the timing capture module processes the timestamped data samples to produce orientation and velocity increments. In an embodiment, the SDI system also includes a priority buffer for storing the data stream produced by the timing capture module prior to provision of the data stream to the SDI module. The SDI module may output SDI motion increments at a time specified in an output request via an output request sample placed in the incoming data stream and having a timestamp in the common time-base.

An attitude estimator system is provided. The attitude estimator system includes a navigation system, a Kalman filter, and a form observations module. The navigation system receives input from a first accelerometer and gyroscope, a second accelerometer and gyroscope, and a third accelerometer and gyroscope. The form observations module receives input from at least one high performance accelerometer and/or gyroscope; forms and outputs at least one of velocity-derived observations and attitude-derived observations. The Kalman filter processes by at least one of: inputting the velocity-derived observations formed in the form observations module, rotating the velocity-derived observation into a sensor-frame, and zeroing gains associated with at least one low performance accelerometer and/or gyroscope; or inputting the attitude-derived observations that are based on output from at least one of the first high performance accelerometer, the first high performance gyroscope, and the second high performance accelerometer.

A sensor module includes a first sensor device, a second sensor device, and a microcontroller. The first sensor device includes a first synchronization terminal to which an external synchronization signal or a synchronization signal which is a signal based on the external synchronization signal is input, a first interface outputs first measurement data to the microcontroller on the basis of the synchronization signal which is input to the first synchronization terminal, the second sensor device includes a second synchronization terminal to which the synchronization signal is input, and a second interface outputs second measurement data to the microcontroller on the basis of the synchronization signal which is input to the second synchronization terminal.

A sensor module includes an X-axis angular velocity sensor device that outputs digital X-axis angular velocity data, a Y-axis angular velocity sensor device that outputs digital Y-axis angular velocity data, a Z-axis angular velocity sensor device that outputs digital Z-axis angular velocity data, an acceleration sensor device that outputs digital X-axis, Y-axis, and Z-axis acceleration data, a microcontroller, a first digital interface bus that electrically connects the X-axis angular velocity sensor device, the Y-axis angular velocity sensor device, and the Z-axis angular velocity sensor device to a first digital interface, and a second digital interface bus that electrically connects the acceleration sensor device to a second digital interface.

An inertial measurement system comprising at least one sensor cluster comprising a plurality of inertial sensors for sampling at least one of acceleration and angular velocity of said at least one sensor cluster with respect to each axis in a plurality of axes of a reference frame, and for producing individual outputs associated with said at least one of acceleration and angular velocity, at least three of said inertial sensors said sampling with respect to each same respective said axis; and a processing engine for receiving said individual outputs, combining said individual outputs to yield respective combined outputs, detecting which of said individual outputs diverges from at least one of their inter-comparison, and its respective combined output, according to a decision rule, said processing engine configured to dynamically self-calibrate a parameter that includes individual scale factor of those said inertial sensors whose said individual outputs were detected to diverge.

The invention relates to a navigational aid method for an inertial navigation system including at least one inertial sensor (4) having a sensitive axis (X-X), each inertial sensor (4) comprising an ASG gyroscope (8) able to deliver an ASG signal representative of a rotation about the corresponding sensitive axis (X-X), and a MEMS gyroscope (10) able to deliver a MEMS signal representative of a rotation about the corresponding sensitive axis (X-X), the method including the steps of: between a first date and a subsequent third date, calculating a path from the MEMS signals; from the third date, calculating the path from the ASG signals; estimating a bias vector introduced by the MEMS gyroscopes (10), from the MEMS signals and ASG signals; at a fourth date subsequent to the third date, resetting the path.