Honeywell International Inc.

Andrew Stewart

Vijay Venkataraman

Alberto Speranzon

Shashank Shivkumar

Richard S Russell

Carl A Dins

Markus Hans Gnerlich

Thomas Jakel

Vegnesh Jayaraman

Patrick Duffy

Ctibor Mazal

Sai Krishnan Chandrasekar

Christopher J Mauer

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

Tightly coupled end-to-end multi-sensor fusion with integrated compensation

Systems and methods for multiple inertial measurement unit sensor fusion using machine learning are provided herein. In certain embodiments, a system includes inertial sensors that produce inertial measurements, a memory unit that stores a fusion model produced by at least one machine learning algorithm, and a processor that receives inertial measurements, where the processor applies the fusion model to the inertial measurements. The fusion model directs the processor to extract features from the inertial measurements, and to select inertial measurements based on a sensor in the plurality of inertial sensors that produced the inertial measurements. Also, the fusion model directs the processor to apply weights to the selected inertial measurements based on the extracted features, to apply compensation coefficients to the selected inertial measurements, and to fuse the selected inertial measurements into an inertial navigation solution.

Multiple inertial measurement unit sensor fusion using machine learning

Systems and methods for the external aiding of inertial navigation systems are described herein. In certain embodiments, a device includes an inertial navigation system. In some embodiments, the inertial navigation system includes one or more inertial sensors and an input interface for receiving measurements. In further embodiments, the measurements include at least one of delta attitude and/or delta position measurements from an external system, and position and attitude information in an arbitrary map frame. In certain embodiments, the inertial navigation system includes a computation device that is configured to calibrate the errors from the one or more inertial sensors using the received measurements.

Delta position and delta attitude aiding of inertial navigation system

A method for inertial sensor error modeling and compensation comprises obtaining multiple bias drift datasets for an elapsed time period for one or more gyroscopes; generating a 3D bias drift data plot using the multiple bias drift datasets; generating a partial bias drift data image based on the 3D bias drift data plot; and inputting the partial bias drift data image into a machine learning algorithm to predict how bias drift evolves over time for the gyroscopes. The machine learning algorithm uses the partial bias drift data image, the elapsed time period, and temperature history to compute a predicted bias over temperature with respect to time, to thereby predict bias drift data for a future time period for the gyroscopes. The machine learning algorithm outputs a completed bias drift image that represents drift data from the elapsed time period and the predicted bias drift data for the future time period.

Inertial sensor error modeling and compensation, and system for lifetime inertial sensor calibration and navigation enhancement

Techniques for detecting motion of a vehicle are disclosed. Optical flow techniques are applied to the entirety of the received images from an optical sensor mounted to a vehicle. Motion detection techniques are then imposed on the optical flow output to remove image portions that correspond to objects moving independent from the vehicle and determine the extent, if any, of movement by the vehicle from the remaining image portions. Motion detection can be performed via a machine learning classifier. In some aspects, motion can be detected by extracting the depth of received images in addition to optical flow. In additional or alternative aspects, the optical flow and/or motion detection techniques can be implemented by at least one artificial neural network.

Machine learning and vision-based approach to zero velocity update object detection

An Inertial Measurement Unit (IMU), method, and navigation system are disclosed. For example, the method includes receiving a plurality of sensor values from an IMU, loading the plurality of sensor values and a plurality of true sensor values into a deep learning algorithm, and training the deep learning algorithm to enhance the accuracy of the plurality of sensor values utilizing the plurality of true sensor values.

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Chanhassen, MN, United States of America

Andrew Stewart