University of Minnesota

Dimitrios G Kottas

Stergios I Roumeliotis

Joel Hesch

Chao X Guo

Kejian J Wu

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.

Extrinsic parameter calibration of a vision-aided inertial navigation system

A vision-aided inertial navigation system (VINS) is described in which a filter-based sliding-window estimator implements an Iterative Kalman Smoother (IKS) to track the 3D motion of a VINS system, such as a mobile device, in real-time using visual and inertial measurements.

Iterative Kalman Smoother for robust 3D localization for vision-aided inertial navigation

This disclosure describes techniques for reducing or eliminating estimator inconsistency in vision-aided inertial navigation systems (VINS). For example, an observability-constrained VINS (OC-VINS) is described which enforce the unobservable directions of the system to prevent spurious information gain and reduce inconsistency.

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Minneapolis, MN, United States of America

Dimitrios G Kottas