Abstract: 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.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: 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.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: Systems and devices are disclosed for providing sensor data First and second sensors integrated with a portable device may have multiple axes of measurement By combining data from the first sensor and the second sensor, a synthetic signal may be generated for a first axis of the first sensor.

Abstract: Systems and devices are disclosed for providing sensor data First and second sensors integrated with a portable device may have multiple axes of measurement By combining data from the first sensor and the second sensor, a synthetic signal may be generated for a first axis of the first sensor.

Abstract: Systems and methods are disclosed for calibrating a sensor. A recursive least squares estimation may be performed to update a mean and a covariance matrix for samples of data from a motion sensor and a bias estimate for the motion sensor may be derived from the mean and covariance matrix. The motion sensor may be an gyroscope, an accelerometer or a magnetometer.

Abstract: Systems and methods are disclosed for predicting a future orientation of a device. A future motion sensor sample may be predicted using a plurality of motion sensor samples for the device up to a current time. After determining the current orientation of the device, the predicted motion sensor sample may be used to predict a future orientation of the device at one or more times.

Abstract: Systems and methods are disclosed for calibrating a sensor. A recursive least squares estimation may be performed to update a mean and a covariance matrix for samples of data from a motion sensor and a bias estimate for the motion sensor may be derived from the mean and covariance matrix. The motion sensor may be an gyroscope, an accelerometer or a magnetometer.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: A method includes generating motion data by receiving a gyroscope data from a gyroscope sensor, performing integration using the gyroscope data and generating an integrated gyroscope data using a first processor. The method further includes receiving a data from one or more sensors, other than the gyroscope sensor, and performing sensor fusion using the integrated gyroscope data and the data to generate motion data using a second processor.

Abstract: The disclosed system and method for a mobile device combines information derived from onboard sensors with conventional signal processing information derived from a speech or audio signal to assist in noise and echo cancellation. In some implementations, an Angle and Distance Processing (ADP) module is employed on a mobile device and configured to provide runtime angle and distance information to an adaptive beamformer for canceling noise signals, provides a means for building a table of filter coefficients for adaptive filters used in echo cancellation, provides faster and more accurate Automatic Gain Control (AGC), provides delay information for a classifier in a Voice Activity Detector (VAD), provides a means for automatic switching between a speakerphone and handset mode of the mobile device, or primary microphone and reference microphones and assists in separating echo path changes from double talk.