Abstract: Some implementations relate to methods, systems, and computer-readable media to display a rigid body object within a virtual environment. In some implementations, the method includes obtaining a model representation of the rigid body object, obtaining a state of the rigid body object in the virtual environment, determining a plurality of constraints that act on the rigid body object, determining a plurality of Jacobians and a plurality of Hessians, wherein each Jacobian of the plurality of Jacobians and each Hessian of the plurality of Hessians is associated with a corresponding constraint of the plurality of constraints, applying a two-stage primal solver to a set of equations of motion for the rigid body object based on the plurality of Jacobians and the plurality of Hessians, and displaying the rigid body object in the updated position in the virtual environment.

Abstract: Some implementations relate to methods, systems, and computer-readable media to display a rigid body object within a virtual environment. In some implementations, the method includes obtaining a model representation of the rigid body object, obtaining a state of the rigid body object in the virtual environment, determining a plurality of constraints that act on the rigid body object, determining a plurality of Jacobians and a plurality of Hessians, wherein each Jacobian of the plurality of Jacobians and each Hessian of the plurality of Hessians is associated with a corresponding constraint of the plurality of constraints, applying a two-stage primal solver to a set of equations of motion for the rigid body object based on the plurality of Jacobians and the plurality of Hessians, and displaying the rigid body object in the updated position in the virtual environment.

Abstract: Some implementations relate to methods, systems, and computer-readable media to display a rigid body object within a virtual environment. In some implementations, the method includes obtaining a model representation of the rigid body object, obtaining a state of the rigid body object in the virtual environment, determining a plurality of constraints that act on the rigid body object, determining a plurality of Jacobians and a plurality of Hessians, wherein each Jacobian of the plurality of Jacobians and each Hessian of the plurality of Hessians is associated with a corresponding constraint of the plurality of constraints, applying a two-stage primal solver to a set of equations of motion for the rigid body object based on the plurality of Jacobians and the plurality of Hessians, and displaying the rigid body object in the updated position in the virtual environment.

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Disclosed herein are methods and systems for mapping irregular features. In an embodiment, a computer-implemented method may include obtaining tracking data that has dead reckoning tracking data for a tracked subject along a path and performing shape correction on the tracking data to provide a first estimate of the path.

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.