Abstract: A method for computing a correction to a compass heading for a portable device worn or carried by a user is described. The method involves determining a heading for the device based on a compass reading, collecting data from one or more sensors, determining if the device is indoors or outdoors based on the collected data, and correcting the heading based on the determination of whether the device is indoors or outdoors.

Abstract: 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.

Abstract: A method for computing a correction to a compass heading for a portable device worn or carried by a user is described. The method involves determining a heading for the device based on a compass reading, collecting data from one or more sensors, determining if the device is indoors or outdoors based on the collected data, and correcting the heading based on the determination of whether the device is indoors or outdoors.

Abstract: 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.

Abstract: 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.

Abstract: A method for determining an environmental pressure change affecting a pressure sensor within a portable device to determine an elevation of the portable device is disclosed. The method involves estimating a location of the mobile device, estimating an atmospheric pressure associated with the mobile device at a server based on data indicative of atmospheric pressure received from the mobile device, and generating the elevation of the mobile device based on the atmospheric pressure associated with the mobile device and reference data indicative of an absolute elevation reference. The absolute elevation determined may be based on the estimated location of the mobile device and elevation data obtained from a reference map.

Abstract: 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.

Abstract: A method for computing a correction to a compass heading for a portable device worn or carried by a user is described. The method involves determining a heading for the device based on a compass reading, collecting data from one or more sensors, determining if the device is indoors or outdoors based on the collected data, and correcting the heading based on the determination of whether the device is indoors or outdoors.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.

Abstract: A method for computing a correction to a compass heading for a portable device worn or carried by a user is described. The method involves determining a heading for the device based on a compass reading, collecting data from one or more sensors, determining if the device is indoors or outdoors based on the collected data, and correcting the heading based on the determination of whether the device is indoors or outdoors.

Abstract: A method for determining an environmental pressure change affecting a pressure sensor within a portable device to determine an elevation of the portable device is disclosed. The method involves estimating a location of the mobile device, estimating an atmospheric pressure associated with the mobile device at a server based on data indicative of atmospheric pressure received from the mobile device, and generating the elevation of the mobile device based on the atmospheric pressure associated with the mobile device and reference data indicative of an absolute elevation reference. The absolute elevation determined may be based on the estimated location of the mobile device and elevation data obtained from a reference map.

Abstract: A method for determining an environmental pressure change affecting a pressure sensor within a portable device to determine an elevation of the portable device is disclosed. The method involves sampling pressure data from at least one stationary pressure sensor in an area surrounding a location of the device, wherein the stationary pressure sensor in not within the portable device. The sampled pressure data is then interpolated to a time interval and a difference is computed between the interpolated pressure data over each time interval to determine a differential pressure. The location of the stationary pressure sensor is determined and the differential pressure is added to a pressure map affecting data near the location. The environmental pressure change is then computed over any interval at the location and subtracted from a pressure measurement of the pressure sensor before computing an elevation of the portable device.

Abstract: A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.

Abstract: A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.

Abstract: A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.

Abstract: A method for detecting a human's steps and estimating the horizontal translation direction and scaling of the resulting motion relative to an inertial sensor is described. When a pedestrian takes a sequence of steps the displacement can be decomposed into a sequence of rotations and translations over each step. A translation is the change in the location of pedestrian's center of mass and a rotation is the change along z-axis of the pedestrian's orientation. A translation can be described by a vector and a rotation by an angle.