Search

Kejian Wu

from Northville, MI
Age ~63
Get Report
Also known as:
Kevin Wu, Kejian Kejian Wu
Related to:
Yanqiu Wu
Connected to:
Ada M Wu, 61Alain W Wu, 56Angelina R Wu, 54Anshih Wu, 36Apple Y Wu, 59Artie K Wu, 71Beilei Wu, 41Caifen Wu, 56Carl G Wu, 44Carol S Wu, 97

Kejian Wu Phones & Addresses

  • Northville, MI
  • Canton, MI
  • Farmington, CT
  • Dublin, OH
  • Minneapolis, MN
  • Alpharetta, GA
  • Gainesville, GA
  • Clear Lake, MN
  • Willowbrook, IL
  • Chicago, IL

Publications

Us Patents

Vision-Aided Inertial Navigation System For Ground Vehicle Localization

View page
US Patent:
20190368879, Dec 5, 2019
Filed:
May 29, 2019
Appl. No.:
16/425422
Inventors:
- Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
Chao Guo - Minneapolis MN, US
Georgios Georgiou - San Francisco CA, US
International Classification:
G01C 21/16
G01C 21/28
G07C 5/08
G06T 7/73
G06T 7/277
G06T 7/246
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.

Square Root Inverse Schmidt-Kalman Filters For Vision-Aided Inertial Navigation And Mapping

View page
US Patent:
20190178646, Jun 13, 2019
Filed:
Dec 7, 2018
Appl. No.:
16/213248
Inventors:
- Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
Tong Ke - Mountain View CA, US
International Classification:
G01C 21/16
G05D 1/02
G01C 21/20
G06T 7/277
G06T 7/73
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. Further, the estimator determines uncertainty data for the state estimates and maintains the uncertainty data as a square root factor of a Hessian matrix.

Square-Root Multi-State Constraint Kalman Filter For Vision-Aided Inertial Navigation System

View page
US Patent:
20190154449, May 23, 2019
Filed:
Jul 21, 2017
Appl. No.:
16/316718
Inventors:
- Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
International Classification:
G01C 21/16
G06T 7/277
G06T 7/70
G06F 17/16
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.

Inverse Sliding-Window Filters For Vision-Aided Inertial Navigation Systems

View page
US Patent:
20170261324, Sep 14, 2017
Filed:
May 22, 2017
Appl. No.:
15/601261
Inventors:
- Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
International Classification:
G01C 21/16
G06T 7/70
G06K 9/00
G06T 7/20
G06K 9/46
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.

Inverse Sliding-Window Filters For Vision-Aided Inertial Navigation Systems

View page
US Patent:
20160327395, Nov 10, 2016
Filed:
Jul 10, 2015
Appl. No.:
14/796574
Inventors:
- Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
International Classification:
G01C 21/16
G06T 7/20
G06K 9/46
G06T 7/00
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.

Extrinsic Parameter Calibration Of A Vision-Aided Inertial Navigation System

View page
US Patent:
20160005164, Jan 7, 2016
Filed:
Feb 21, 2014
Appl. No.:
14/768733
Inventors:
- St. Paul MN, US
Dimitrios G. Kottas - Minneapolis MN, US
Kejian J. Wu - Minneapolis MN, US
International Classification:
G06T 7/00
H04N 13/02
G01C 21/16
B25J 5/00
B25J 9/16
G06T 7/20
G06F 3/0346
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.
Control profile
Kejian Wu from Northville, MI, age ~63 Get Report