Ceva Technologies, Inc.

Bryan A Cook

Yun Li

Mark Turner

Douglas Charles Carlson

Zhanlue Zhao

Seth Sternberg

Friedrich Geck

Peter Christopher Wood

Steven Francz

Chao Wu

Stephen Scheirey

Chad Lucien

Systems and methods provide for estimating a trajectory of a robot by fusing a plurality of robot velocity measurements from a plurality of robot sensors located within a robot to generate a fused robot velocity based on the accuracy of those robot velocity measurements and applying Kalman filtering to the fused robot velocity to compute a current robot location.

Planar robots dead-reckoning with optical flow, wheel encoder and inertial measurement unit

Methods, apparatus, and systems are provided for calibrating an optical flow (OF) sensor by using an inertial measurement unit (IMU) measurements in a planar robot system.

Methods and apparatus for dynamic optical flow sensor calibration by IMU in planar robot systems

A method for calibrating zero rate offset (ZRO) of a first inertial sensor located on a device, the method includes determining a stability level of the device based on information associated with at least one non-inertial sensor located on the device; and performing a calibration of the ZRO of the first inertial sensor when the stability level is above a threshold.

Methods and apparatus for calibrating the zero rate output of a sensor

Methods, apparatus, and systems directed to calibrating a magnetometer. Among such are methods that use only the horizontal components of magnetometer measurements. Then, planar calibrated magnetic field output measurements can be used in sensor fusion with data from gyroscope(s) and accelerometer(s). In other embodiments, heading information from the planar calibrated magnetic field is fused with the heading calculated from gyroscope integration. Other such methods include any of generating combined information including 6-axis fusion information and magnetometer information; accumulating values for any of a first matrix [T] and a first vector [U] according to the combined information; generating a second matrix [{circumflex over (K)}] according to any of a pseudo-inverse of the first matrix [T] and the first vector [U]; generating any of a scale and skew matrix and an offset according to the first matrix [{circumflex over (K)}]; and calibrating the magnetometer according to any of the scale and skew matrix and the offset.

Methods and apparatus for planar magnetometer calibration, heading determination, gyroscope assisted magnetometer amplitude calibration, magnetometer amplitude and alignment calibration, magnetometer mapping, and sensor fusion

Method, apparatus, and systems for roll angle estimation for an input/gesture device (IGD) are disclosed. The method includes estimating a first axis vector of the input/gesture device (IGD) relative to the surface of the mobile device (MD), as a first frame of reference, using readings from a first type of sensor; and estimating the first axis vector of the IDG relative to a different, second frame of reference using a gyroscope integration of readings from the 3-axis gyroscope. The method further includes aligning the first axis vector of the IDG in the first frame of reference with the first axis vector of the IDG in the second frame of reference; minimizing a change in the alignment of the first axis vectors between the first frame of reference and the second frame of reference; and determining the roll angle of the IDG relative to the surface of the MD.

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Silver Spring, MD, United States of America

Bryan A Cook