Abstract: An integrated navigation solution is provided for a device within a moving platform. Motion sensor data from a sensor assembly of the device is obtained, optical samples from at least one optical sensor for the platform are obtained and map information for an environment encompassing the platform is obtained. Correspondingly, an integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique, wherein the nonlinear state estimation technique uses a nonlinear measurement model for optical sensor data. Generating the integrated navigation solution includes using the sensor data with the nonlinear state estimation technique and integrating the optical sensor data directly by updating the nonlinear state estimation technique using the nonlinear measurement model and the map information. The integrated navigation solution is then provided.

Abstract: Vehicle body tilt, representing a difference between a vehicle body frame of reference and a wheel-base frame of reference, is determined by obtaining information from sensor assemblies for the vehicle body and for the wheel-base. Navigational solutions are generated for the sensor assemblies using motion sensor data from the assemblies and absolute navigational information. Correspondingly, vehicle body tilt is determined based at least in part on the vehicle body navigation solution and the wheel-base navigation solution.

Abstract: Feedback for map information is based on an integrated navigation solution for a device within a moving platform using obtained motion sensor data from a sensor assembly of the device, obtained radar measurements for the platform and obtained map information for an environment encompassing the platform. An integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique that uses a nonlinear measurement model for radar measurements. The map information is assessed based at least in part on the integrated navigation solution and radar measurements so that feedback for the map information can be provided.

Abstract: An integrated navigation solution is provided for a device within a moving platform. Motion sensor data is obtained from a sensor assembly of the device, radar measurements for the platform are obtained and map information for an environment encompassing the platform is obtained. Correspondingly, an integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique, wherein the nonlinear state estimation technique uses a nonlinear measurement model for radar measurements. Generating the integrated navigation solution includes using the sensor data with the nonlinear state estimation technique and integrating the radar measurements directly by updating the nonlinear state estimation technique using the nonlinear measurement models and the map information. The integrated navigation solution is then provided.

Abstract: An integrated navigation solution is provided for a device within a moving platform. Motion sensor data from a sensor assembly of the device is obtained, optical samples from at least one optical sensor for the platform are obtained and map information for an environment encompassing the platform is obtained. Correspondingly, an integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique, wherein the nonlinear state estimation technique uses a nonlinear measurement model for optical sensor data. Generating the integrated navigation solution includes using the sensor data with the nonlinear state estimation technique and integrating the optical sensor data directly by updating the nonlinear state estimation technique using the nonlinear measurement model and the map information. The integrated navigation solution is then provided.

Abstract: An odometry solution for a device within a moving platform is provided using a deep neural network. Radar measurements may be obtained, such that static objects are detected based at least in part on the obtained radar measurements. Odometry information for the platform is estimated based at least in part on the detected static objects and the obtained radar measurements.

Abstract: Feedback for map information is based on an integrated navigation solution for a device within a moving platform using obtained motion sensor data from a sensor assembly of the device, obtained radar measurements for the platform and obtained map information for an environment encompassing the platform. An integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique that uses a nonlinear measurement model for radar measurements. The map information is assessed based at least in part on the integrated navigation solution and radar measurements so that feedback for the map information can be provided.

Abstract: Systems and methods are disclosed for characterizing on foot motion of a user with a portable device by obtaining parameters sufficient to characterize the on foot motion of the user from multiple sensor assemblies. Each additional sensor assembly may be independent of each other. The characterization of on foot motion is provided by synthesizing the parameters from the sensor assemblies. Characterization of on foot motion may include detecting a step, estimating step length, or both.

Abstract: An integrated navigation solution is provided for a device within a moving platform. Motion sensor data is obtained from a sensor assembly of the device, radar measurements for the platform are obtained and map information for an environment encompassing the platform is obtained. Correspondingly, an integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique, wherein the nonlinear state estimation technique uses a nonlinear measurement model for radar measurements. Generating the integrated navigation solution includes using the sensor data with the nonlinear state estimation technique and integrating the radar measurements directly by updating the nonlinear state estimation technique using the nonlinear measurement models and the map information. The integrated navigation solution is then provided.

Abstract: The present disclosure relates to a method and system for providing and enhanced navigation solution of a device within a platform (such as for example person, vehicle, or vessel of any type), wherein the device is within the platform and may be strapped or non-strapped to the platform, wherein in case of non-strapped the mobility of the device may be constrained or unconstrained within the platform, wherein the enhancement of the navigation solution is through multiple sensor assemblies, wherein the multiple sensors assemblies are not necessarily all on the device, and wherein the multiple sensors assemblies are within the platform. This method works for a navigation solution utilizing measurements from sensors (such as for example, accelerometers, gyroscopes, magnetometers etc.) whether in the presence or in the absence of absolute navigational information (such as, for example, GNSS or WiFi positioning).

Abstract: The present disclosure relates to a method and apparatus for determining the misalignment between a device and a platform (such as for example a vessel or vehicle) using radius of rotation of the device, wherein mobility of the device may be constrained or unconstrained within the platform. The device may be moved or tilted to any orientation within the platform and still provide a seamless navigation solution without degrading the performance of this navigation solution. This method can utilize measurements (readings) from sensors (such as for example, accelerometers, gyroscopes, etc.) whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning).

Abstract: The present disclosure relates to a method and system for estimating varying step length for on foot motion (such as for example walking or running). The present method and apparatus is able to be used in anyone or both of two different phases depending on the embodiment. The first phase is a model-building phase done offline to obtain the nonlinear model for the step length as a function of different parameters that represent human motion dynamics, the model is built using a nonlinear system identification technique. In the second phase the nonlinear model is used to calculate the step length from the different parameters that represent human motion dynamics used as input to the model. These parameters are obtained from sensors readings from the sensors in the apparatus. This second phase is the more frequent usage of the present method and apparatus for a variety of applications.

Abstract: The navigation solution of a device may be enhanced by perforating multiple pass smoothing. Forward and backward processing of the input data may be performed to derive interim navigation solutions. One or more quantities of the interim navigation solutions may be combined to smooth the quantities. At least one additional pass of forward and backward processing may then be performed using quantities of the navigation solution that were combined to enhance the interim navigation solutions. Next, at least one uncombined quantity of the navigation solution from the enhanced interim navigation solution is combined to provide an enhanced smoothed navigation solution. Additional passes may be performed to combine other quantities of the navigation solution as desired.

Abstract: The navigation solution of a device may be enhanced by perforating multiple pass smoothing. Forward and backward processing of the input data may be performed to derive interim navigation solutions. One or more quantities of the interim navigation solutions may be combined to smooth the quantities. At least one additional pass of forward and backward processing may then be performed using quantities of the navigation solution that were combined to enhance the interim navigation solutions. Next, at least one uncombined quantity of the navigation solution from the enhanced interim navigation solution is combined to provide an enhanced smoothed navigation solution. Additional passes may be performed to combine other quantities of the navigation solution as desired.

Abstract: The present disclosure relates to a method and apparatus for determining the misalignment between a device and a platform (such as for example a vessel or vehicle) using radius of rotation of the device, wherein mobility of the device may be constrained or unconstrained within the platform. The device may be moved or tilted to any orientation within the platform and still provide a seamless navigation solution without degrading the performance of this navigation solution. This method can utilize measurements (readings) from sensors (such as for example, accelerometers, gyroscopes, etc.) whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning).

Abstract: The present disclosure relates to a method and system for providing and enhanced navigation solution of a device within a platform (such as for example person, vehicle, or vessel of any type), wherein the device is within the platform and may be strapped or non-strapped to the platform, wherein in case of non-strapped the mobility of the device may be constrained or unconstrained within the platform, wherein the enhancement of the navigation solution is through multiple sensor assemblies, wherein the multiple sensors assemblies are not necessarily all on the device, and wherein the multiple sensors assemblies are within the platform. This method works for a navigation solution utilizing measurements from sensors (such as for example, accelerometers, gyroscopes, magnetometers etc.) whether in the presence or in the absence of absolute navigational information (such as, for example, GNSS or WiFi positioning).

Abstract: Systems and methods are disclosed for characterizing on foot motion of a user with a portable device by obtaining parameters sufficient to characterize the on foot motion of the user from multiple sensor assemblies. Each additional sensor assembly may be independent of each other. The characterization of on foot motion is provided by synthesizing the parameters from the sensor assemblies. Characterization of on foot motion may include detecting a step, estimating step length, or both.

Abstract: The present disclosure relates to a method and system for estimating varying step length for on foot motion (such as for example walking or running). The present method and apparatus is able to be used in anyone or both of two different phases. In some embodiments, the first phase is used. In some other embodiments, the second phase is used. In a third group of embodiments, the first phase is used, and then the second phase is used. The first phase is a model-building phase done offline to obtain the nonlinear model for the step length as a function of different parameters that represent human motion dynamics. A nonlinear system identification technique is used for building this model. In the second phase the nonlinear model is used to calculate the step length from the different parameters that represent human motion dynamics used as input to the model. These parameters are obtained from sensors readings from the sensors in the apparatus.