University of California

University of Minnesota

Anastasios I Mourikis

Stergios I Roumeliotis

Mingyang Li

Localization and navigation systems and techniques are described. An electronic device comprises a processor configured to maintain a state vector storing estimates for a position of the electronic device at poses along a trajectory within an environment along with estimates for positions for one or more features within the environment. The processor computes, from the image data, one or more constraints based on features observed from multiple poses of the electronic device along the trajectory, and computes updated state estimates for the position of the electronic device in accordance with the motion data and the one or more computed constraints without computing updated state estimates for the features for which the one or more constraints were computed.

Vision-aided inertial navigation

Localization and navigation systems and techniques are described. An electronic device comprises a processor configured to apply an extended Kalman filter (EKF) as the electronic device traverses the trajectory. The extended Kalman filter is configured to maintain a state vector storing estimates for a position of the electronic device at poses along a trajectory within an environment along with estimates for positions for one or more features within the environment. The EKF computes constraints based on features observed from multiple poses along the trajectory, and updates, in accordance with motion data and the one or more computed constraints, the estimates within the state vector of the extended Kalman filter while excluding, from the state vector, state estimates for positions within the environment for the features that were observed from the multiple poses and for which the constraints were computed.

Extended kalman filter for 3D localization and vision-aided inertial navigation

A system and method for a 3-D motion estimation and online temporal calibration having one or more than one processor, one or more than one camera, one or more than one inertial sensor, a storage for storing data and instructions. The stored instructions comprise a filter estimation module, high-precision navigation instructions and consistent state estimates in scenarios involving features, with both constant and time-varying offsets from the filter estimation module.

3-D motion estimation and online temporal calibration for camera-IMU systems

A mobile platform having a camera for outputting image data of features with unknown locations, an inertial measurement unit for outputting inertial measurements of the features, where the coordinates of the features are unknown, a processor, storage and an extended-Kalman filter-based estimator executable on the processor for processing the inertial measurement and features of the image data, where a state vector of the estimator contains a sliding window of states for determining the position and orientation of the mobile platform.

Method for processing feature measurements in vision-aided inertial navigation

A motion estimation and correction system and methods are shown comprising: an image acquisition device configured to acquire an image via scanning an image frame over a period of time, an inertial measurement unit configured to measure at least one of a position, an orientation, and a movement of the image acquisition device during the period of time and output an indication of the movement as detected; and a state estimation module, operatively coupled to the inertial measurement unit and the image acquisition device, configured to estimate a state related to at least one of position and orientation of the image acquisition device based on the at least one of the position, the orientation, and the movement. In one example, systems and methods include using an inertial measurement unit along with an image acquisition device. In one example, the image acquisition device includes a rolling-shutter camera.

Method for camera motion estimation and correction

A hybrid estimator system using visual and inertial sensors for real-time pose tracking on devices with limited processing power using at least one processor, a memory, a storage and communications through a protocol and one or more than one software module for a hybrid estimator, real-time algorithm selection to process different measurements, statistical learning for these characteristics to compute the expected device computing cost of any strategy for allocating measurements to algorithms, and algorithm selection based on the statistical learning module.

Real-time pose estimation system using inertial and feature measurements

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked.

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Minneapolis, MN, United States of America

Anastasios I Mourikis