Trx Systems, Inc.

Benjamin E Funk

Carole Teolis

John George Karvounis

Jared Napora

Travis Young

Daniel Hakim

Kamiar Kordari

Dan Hakim

Christopher Giles

Amrit Bandyopadhyay

Carol Ann Politi

Eric Asher Kohn

Gilmer Blankenship

Ruchika Verma

Tim Wang

Stuart Woodbury

Jonathan Fetter Degges

Daniel Franchy

John Riley Miller

Neil Goldsman

Brian Beisel

Carole A Teolis

Gilmer L Blankeship

Jonathan Morton Fetter-Degges

Gilmer L Blankenship

Systems, methods and instructions for creating building models of physical structures is disclosed. The building model may be a collection of floors defined by outlines containing regions that may be offset relative to a main region, and a collection of connectors. Connectors may have connection points for tracking, routing and sizing. Connectors may indicate elevation changes through georeferenced structural features. Signal elements may also be features that provide corrections when tracking. Feature descriptors are data that describes the structural configuration and signal elements enabling them to be matched to previously collected data in a database. User interface elements assist a user of a tracking device in collecting floor information, structural features and signal features and validating certain collected information based on previously known information. The height of floors may also be inferred based on sensor data from the tracking device.

Methods for generating and updating building models

A computer implemented method of validating an output from a GNSS at a receiver including a fusion system comprising location sensors. A location estimate and a location error estimate are computed. A navigation update including a sensor location estimate and sensor location error estimate is also computed with the fusion system based on sensor measurements from the location sensors. A determination is made as to whether or not GNSS filters should be applied based at least on the location estimate, the sensor location estimate, and the sensor location error estimate. When GNSS filters should be applied, the location estimate and/or the location error estimate may be adjusted or rejected and a new navigation update may be computed with the fusion system based on the adjustment or rejection. When the GNSS filters should not be applied, the new navigation update is computed with the location estimate and the location error estimate.

System and method for validating GNSS location input

Validating and updating building models with path data

A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building.

Crowd sourced mapping with robust structural features

This disclosure provides techniques for the creation of maps of indoor spaces. In these techniques, an individual or a team with no mapping or cartography expertise can contribute to the creation of maps of buildings, campuses or cities. An indoor location system can track the location of contributors in the building. As they walk through indoor spaces, an application may automatically create a map based on data from motion sensors by both tracking the location of the contributors and also inferring building features such as hallways, stairways, and elevators based on the tracked contributors' motions as they move through a structure. With these techniques, the process of mapping buildings can be crowd sourced to a large number of contributors, making the indoor mapping process efficient and easy to scale up.

Collaborative creation of indoor maps

A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building, to indicate signal strength throughout different parts of the building mode, and to illustrate a path of each tracked device associated with signal strength and other annotations.

Mapping complex building models

Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Methods for resolving a location of tracked personnel or assets based on elevation or terrain constraints

Methods for resolving a location of tracked personnel or assets and refining path data

Methods resolving a location of tracked personnel or assets based on elevation

Disclosed herein are methods and systems for fusion of sensor and map data using constraint based optimization. In an embodiment, a computer-implemented method may include obtaining tracking data for a tracked subject, the tracking data including data from a dead reckoning sensor; obtaining constraint data for the tracked subject; and using a convex optimization method based on the tracking data and the constraint data to obtain a navigation solution. The navigation solution may be a path and the method may further include propagating the constraint data by a motion model to produce error bounds that continue to constrain the path over time. The propagation of the constraint data may be limited by other sensor data and/or map structural data.

Fusion of sensor and map data using constraint based optimization

Methods resolving the elevation of a tracked personnel or assets

Growing community of inventors

Ellicott City, MD, United States of America

Benjamin E Funk