A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

A navigation apparatus determines an estimated position of an object. Navigation information that includes a sequence of quantized measurements is received. Each quantized measurement has a corresponding quantization error that is negatively correlated with the quantization error of a prior quantized measurement. The apparatus iteratively performs a navigation update operation that includes determining a state and a covariance matrix of the object for a current iteration based on a state and covariance matrix in a prior iteration. The state includes an end quantization error and a start quantization error. The covariance matrix includes end and start covariance values corresponding to the end and start quantization errors, respectively. Determining the state and the covariance matrix for the current iteration includes replacing the start quantization error with the end quantization error determined in a prior iteration, and updating the state and the covariance matrix via a Kalman filter update operation.

An inertial measurement system for a spinning projectile comprising: first (roll), second and third gyros with axes arranged such that they define a three dimensional coordinate system; at least a first linear accelerometer; a controller, arranged to: compute a current projectile attitude comprising a roll angle, a pitch angle and a yaw angle; compute a current velocity vector from the accelerometer; combine a magnitude of said velocity vector with an expected direction for said vector to form a pseudo-velocity vector; provide the velocity vector and the pseudo-velocity vector to a Kalman filter that outputs a roll gyro scale factor error calculated as a function of the difference between the velocity vector and the pseudo-velocity vector; and apply the roll gyro scale factor error from the Kalman filter as a correction to the output of the roll gyro.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user/wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.

A system and method are provided for determining the orientation of an inertial measurement unit (IMU). The method calculates a gyroscopic quaternion, and when the IMU accelerometer reading is about equal to gravity (1 G), a field quaternion is calculated using IMU accelerometer readings. Estimates are made of angular orientation errors due to IMU angular velocity and linear acceleration, and these angular orientation errors are used to selectively mix the gyroscopic quaternion and field quaternion to supply a current sample quaternion. Alternatively, if the accelerometer reading is not about equal to 1 G, the gyroscopic quaternion is used as the current sample quaternion. In one aspect, IMU gyroscope readings and IMU accelerometer readings are calibrated in response to determining a lack of IMU movement. Near-zero gyroscope reading jitter is removed by setting the IMU gyroscopic reading to zero, when the gyroscopic reading is near zero.

An approach to determining vehicle usage makes use of a sensor that provides a vibration signal associated with the vehicle, and that vibration signal is used to infer usage. Usage can include distance traveled, optionally associated with particular ranges of speed or road type. In a calibration phase, auxiliary measurements, for instance based on GPS signals, are used to determine a relationship between the vibration signal and usage. In a monitoring phase, the determined relationship is used to infer usage from the vibration signal.

A posture estimation device includes an acquisition part acquires information of angular velocities and accelerations from a plurality of sensors that detects angular velocities and accelerations and that are attached to a plurality of locations on an estimation object, a conversion part that converts information acquired by the acquisition part into information of a standard coordinate system from a sensor coordinate system, an integrating part that calculates an orientation of a reference area of the estimation object as a part of a posture of the estimation object by integrating the converted angular velocities, and a correction part, assuming a representative plane passing through a reference area included in the estimation object, corrects the converted angular velocities of the reference area so that a normal line of the representative plane and an orientation of the reference area calculated by the integrating part approaches to directions that are perpendicular to each other.

A mobile robotic device has a motion sensor assembly configured to provide data for deriving a navigation solution for the mobile robotic device. The mobile robotic device temperature is determined for at least two different epochs so that an accumulated heading error of the navigation solution can be estimated based on the determined temperature at the at least two different epochs. A calibration procedure is then performed for at least one sensor of the motion sensor assembly when the estimated accumulated heading error is outside a desired range.

Techniques for, among other things, detecting faults associated with inertial measurement units (IMUs) of a vehicle when multiple IMUs are coupled to the vehicle are described herein. The techniques may include receiving first data from a first IMU of the vehicle and receiving second data from a second IMU of the vehicle. Based at least in part on the first data and the second data, a rotation of the first IMU relative to the second IMU may be calculated. The calculated rotation between the first IMU and the second IMU may be indicative of a fault associated with the first IMU or the second IMU. In response to detecting the fault, an action may be performed with respect to the first IMU or the second IMU to correct for the fault.

A method of mitigating filter inconsistency of a heading estimate in an inertial navigation system is provided. The method includes inputting inertial measurements from at least one low-performance inertial measurement unit (IMU) in the inertial navigation system; inputting an initial-heading from at least one heading-information source; inputting an initial-heading uncertainty level associated with an error in the inputted initial-heading; initializing an initial-heading estimate with the inputted initial-heading; initializing an initial-heading uncertainty estimate with the inputted initial-heading uncertainty level; initializing an accumulated heading-change estimate to zero at a startup of the at least one low-performance IMU; and initializing an accumulated heading-change uncertainty estimate with an initial accumulated heading-change uncertainty level that is a value less than the inputted initial-heading uncertainty level; and periodically updating the accumulated heading-change estimate with the inertial measurements input from the at least one low-performance IMU.