Patents by Inventor Kejian J. Wu

Kejian J. Wu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240230335
    Abstract: A vision-aided inertial navigation system (VINS) comprises an image source for producing image data along a trajectory. The VINS further comprises an inertial measurement unit (IMU) configured to produce IMU data indicative of motion of the VINS and an odometry unit configured to produce odometry data. The VINS further comprises a processor configured to compute, based on the image data, the IMU data, and the odometry data, state estimates for a position and orientation of the VINS for poses of the VINS along the trajectory. The processor maintains a state vector having states for a position and orientation of the VINS and positions within the environment for observed features for a sliding window of poses. The processor applies a sliding window filter to compute, based on the odometry data, constraints between the poses within the sliding window and compute, based on the constraints, the state estimates.
    Type: Application
    Filed: February 15, 2024
    Publication date: July 11, 2024
    Applicant: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu, Chao Guo
  • Patent number: 11994392
    Abstract: A vision-aided inertial navigation system (VINS) implements a square-root multi-state constraint Kalman filter (SR-MSCKF) for navigation. In one example, a processor of a VINS receives image data and motion data for a plurality of poses of a frame of reference along a trajectory. The processor executes an Extended Kalman Filter (EKF)-based estimator to compute estimates for a position and orientation for each of the plurality of poses of the frame of reference along the trajectory. For features observed from multiple poses along the trajectory, the estimator computes constraints that geometrically relate the multiple poses of the respective feature. Using the motion data and the computed constraints, the estimator computes state estimates for the position and orientation of the frame of reference. Further, the estimator determines uncertainty data for the state estimates and maintains the uncertainty data as a square root factor of a covariance matrix.
    Type: Grant
    Filed: October 10, 2022
    Date of Patent: May 28, 2024
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Patent number: 11940277
    Abstract: A vision-aided inertial navigation system (VINS) comprises an image source for producing image data along a trajectory. The VINS further comprises an inertial measurement unit (IMU) configured to produce IMU data indicative of motion of the VINS and an odometry unit configured to produce odometry data. The VINS further comprises a processor configured to compute, based on the image data, the IMU data, and the odometry data, state estimates for a position and orientation of the VINS for poses of the VINS along the trajectory. The processor maintains a state vector having states for a position and orientation of the VINS and positions within the environment for observed features for a sliding window of poses. The processor applies a sliding window filter to compute, based on the odometry data, constraints between the poses within the sliding window and compute, based on the constraints, the state estimates.
    Type: Grant
    Filed: May 29, 2019
    Date of Patent: March 26, 2024
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu, Chao Guo, Georgios Georgiou
  • Publication number: 20230194265
    Abstract: A vision-aided inertial navigation system (VINS) implements a square-root multi- state constraint Kalman filter (SR-MSCKF) for navigation. In one example, a processor of a VINS receives image data and motion data for a plurality of poses of a frame of reference along a trajectory. The processor executes an Extended Kalman Filter (EKF)- based estimator to compute estimates for a position and orientation for each of the plurality of poses of the frame of reference along the trajectory. For features observed from multiple poses along the trajectory, the estimator computes constraints that geometrically relate the multiple poses of the respective feature. Using the motion data and the computed constraints, the estimator computes state estimates for the position and orientation of the frame of reference. Further, the estimator determines uncertainty data for the state estimates and maintains the uncertainty data as a square root factor of a covariance matrix.
    Type: Application
    Filed: October 10, 2022
    Publication date: June 22, 2023
    Applicant: Regents of the University of Minnesota
    Inventors: Stergios l. Roumeliotis, Kejian J. Wu
  • Patent number: 11466990
    Abstract: A vision-aided inertial navigation system (VINS) implements a square-root multi-state constraint Kalman filter (SR-MSCKF) for navigation. In one example, a processor of a VINS receives image data and motion data for a plurality of poses of a frame of reference along a trajectory. The processor executes an Extended Kalman Filter (EKF)-based estimator to compute estimates for a position and orientation for each of the plurality of poses of the frame of reference along the trajectory. For features observed from multiple poses along the trajectory, the estimator computes constraints that geometrically relate the multiple poses of the respective feature. Using the motion data and the computed constraints, the estimator computes state estimates for the position and orientation of the frame of reference. Further, the estimator determines uncertainty data for the state estimates and maintains the uncertainty data as a square root factor of a covariance matrix.
    Type: Grant
    Filed: July 21, 2017
    Date of Patent: October 11, 2022
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Patent number: 10907971
    Abstract: A vision-aided inertial navigation system comprises an image source to produce image data for poses of reference frames along a trajectory, a motion sensor configured to provide motion data of the reference frames, and a hardware-based processor configured to compute estimates for a position and orientation of the reference frames for the poses. The processor executes a square-root inverse Schmidt-Kalman Filter (SR-ISF)-based estimator to compute, for features observed from poses along the trajectory, constraints that geometrically relate the poses from which the respective feature was observed. The estimator determines, in accordance with the motion data and the computed constraints, state estimates for position and orientation of reference frames for poses along the trajectory and computes positions of the features that were each observed within the environment.
    Type: Grant
    Filed: December 7, 2018
    Date of Patent: February 2, 2021
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu, Tong Ke
  • Publication number: 20190368879
    Abstract: A vision-aided inertial navigation system (VINS) comprises an image source for producing image data along a trajectory. The VINS further comprises an inertial measurement unit (IMU) configured to produce IMU data indicative of motion of the VINS and an odometry unit configured to produce odometry data. The VINS further comprises a processor configured to compute, based on the image data, the IMU data, and the odometry data, state estimates for a position and orientation of the VINS for poses of the VINS along the trajectory. The processor maintains a state vector having states for a position and orientation of the VINS and positions within the environment for observed features for a sliding window of poses. The processor applies a sliding window filter to compute, based on the odometry data, constraints between the poses within the sliding window and compute, based on the constraints, the state estimates.
    Type: Application
    Filed: May 29, 2019
    Publication date: December 5, 2019
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu, Chao Guo, Georgios Georgiou
  • Patent number: 10371529
    Abstract: This disclosure describes inverse filtering and square root inverse filtering techniques for optimizing the performance of a vision-aided inertial navigation system (VINS). In one example, instead of keeping all features in the system's state vector as SLAM features, which can be inefficient when the number of features per frame is large or their track length is short, an estimator of the VINS may classify the features into either SLAM or MSCKF features. The SLAM features are used for SLAM-based state estimation, while the MSCKF features are used to further constrain the poses in the sliding window. In one example, a square root inverse sliding window filter (SQRT-ISWF) is used for state estimation.
    Type: Grant
    Filed: May 22, 2017
    Date of Patent: August 6, 2019
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Publication number: 20190178646
    Abstract: A vision-aided inertial navigation system comprises an image source to produce image data for poses of reference frames along a trajectory, a motion sensor configured to provide motion data of the reference frames, and a hardware-based processor configured to compute estimates for a position and orientation of the reference frames for the poses. The processor executes a square-root inverse Schmidt-Kalman Filter (SR-ISF)-based estimator to compute, for features observed from poses along the trajectory, constraints that geometrically relate the poses from which the respective feature was observed. The estimator determines, in accordance with the motion data and the computed constraints, state estimates for position and orientation of reference frames for poses along the trajectory and computes positions of the features that were each observed within the environment.
    Type: Application
    Filed: December 7, 2018
    Publication date: June 13, 2019
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu, Tong Ke
  • Publication number: 20190154449
    Abstract: A vision-aided inertial navigation system (VINS) implements a square-root multi- state constraint Kalman filter (SR-MSCKF) for navigation. In one example, a processor of a VINS receives image data and motion data for a plurality of poses of a frame of reference along a trajectory. The processor executes an Extended Kalman Filter (EKF)- based estimator to compute estimates for a position and orientation for each of the plurality of poses of the frame of reference along the trajectory. For features observed from multiple poses along the trajectory, the estimator computes constraints that geometrically relate the multiple poses of the respective feature. Using the motion data and the computed constraints, the estimator computes state estimates for the position and orientation of the frame of reference. Further, the estimator determines uncertainty data for the state estimates and maintains the uncertainty data as a square root factor of a covariance matrix.
    Type: Application
    Filed: July 21, 2017
    Publication date: May 23, 2019
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Patent number: 10254118
    Abstract: This disclosure describes various techniques for use within a vision-aided inertial navigation system (VINS). A VINS comprises an image source to produce image data comprising a plurality of images, and an inertial measurement unit (IMU) to produce IMU data indicative of a motion of the vision-aided inertial navigation system while producing the image data, wherein the image data captures features of an external calibration target that is not aligned with gravity. The VINS further includes a processing unit comprising an estimator that processes the IMU data and the image data to compute calibration parameters for the VINS concurrently with computation of a roll and pitch of the calibration target, wherein the calibration parameters define relative positions and orientations of the IMU and the image source of the vision-aided inertial navigation system.
    Type: Grant
    Filed: February 21, 2014
    Date of Patent: April 9, 2019
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Dimitrios G. Kottas, Kejian J. Wu
  • Publication number: 20170261324
    Abstract: This disclosure describes inverse filtering and square root inverse filtering techniques for optimizing the performance of a vision-aided inertial navigation system (VINS). In one example, instead of keeping all features in the system's state vector as SLAM features, which can be inefficient when the number of features per frame is large or their track length is short, an estimator of the VINS may classify the features into either SLAM or MSCKF features. The SLAM features are used for SLAM-based state estimation, while the MSCKF features are used to further constrain the poses in the sliding window. In one example, a square root inverse sliding window filter (SQRT-ISWF) is used for state estimation.
    Type: Application
    Filed: May 22, 2017
    Publication date: September 14, 2017
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Patent number: 9658070
    Abstract: This disclosure describes inverse filtering and square root inverse filtering techniques for optimizing the performance of a vision-aided inertial navigation system (VINS). In one example, instead of keeping all features in the system's state vector as SLAM features, which can be inefficient when the number of features per frame is large or their track length is short, an estimator of the VINS may classify the features into either SLAM or MSCKF features. The SLAM features are used for SLAM-based state estimation, while the MSCKF features are used to further constrain the poses in the sliding window. In one example, a square root inverse sliding window filter (SQRT-ISWF) is used for state estimation.
    Type: Grant
    Filed: July 10, 2015
    Date of Patent: May 23, 2017
    Assignee: Regents of the University of Minnesota
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Publication number: 20160327395
    Abstract: This disclosure describes inverse filtering and square root inverse filtering techniques for optimizing the performance of a vision-aided inertial navigation system (VINS). In one example, instead of keeping all features in the system's state vector as SLAM features, which can be inefficient when the number of features per frame is large or their track length is short, an estimator of the VINS may classify the features into either SLAM or MSCKF features. The SLAM features are used for SLAM-based state estimation, while the MSCKF features are used to further constrain the poses in the sliding window. In one example, a square root inverse sliding window filter (SQRT-ISWF) is used for state estimation.
    Type: Application
    Filed: July 10, 2015
    Publication date: November 10, 2016
    Inventors: Stergios I. Roumeliotis, Kejian J. Wu
  • Publication number: 20160005164
    Abstract: This disclosure describes various techniques for use within a vision-aided inertial navigation system (VINS). A VINS comprises an image source to produce image data comprising a plurality of images, and an inertial measurement unit (IMU) to produce IMU data indicative of a motion of the vision-aided inertial navigation system while producing the image data, wherein the image data captures features of an external calibration target that is not aligned with gravity. The VINS further includes a processing unit comprising an estimator that processes the IMU data and the image data to compute calibration parameters for the VINS concurrently with computation of a roll and pitch of the calibration target, wherein the calibration parameters define relative positions and orientations of the IMU and the image source of the vision-aided inertial navigation system.
    Type: Application
    Filed: February 21, 2014
    Publication date: January 7, 2016
    Inventors: Stergios I. Roumeliotis, Dimitrios G. Kottas, Kejian J. Wu
  • Patent number: 5230916
    Abstract: A natural antioxidant for stabilizing polyunsaturated oils is disclosed. This oil-soluble antioxidant is prepared by dissolving ascorbic acid in a polar solvent, dissolving phospholipid in a non-polar solvent and then mixing the ascorbic acid solution with the phospholipid solution. After removing the solvent a product is formed which has anti-oxidant properties and is soluble in non-polar solvents.
    Type: Grant
    Filed: December 23, 1991
    Date of Patent: July 27, 1993
    Assignee: Kabi Pharmacia AB
    Inventors: Stephen S. Chang, Kejian J. Wu