Abstract:
A device implementing a system for estimating device location includes at least one processor configured to receive a first estimated position of the device at a first time. The at least one processor is further configured to capture, using an image sensor of the device, images during a time period defined by the first time and a second time, and determine, based on the images, a second estimated position of the device, the second estimated position being relative to the first estimated position. The at least one processor is further configured to receive a third estimated position of the device at the second time, and estimate a location of the device based on the second estimated position and the third estimated position.
Abstract:
Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.
Abstract:
Systems, methods, devices and subassemblies for creating and delivering a GNSS augmentation service include one or more reference stations for receiving signals transmitted by navigation beacons and an augmentation server coupled to the reference stations. At least one of the reference stations is able to receive at least one of the signals from a low earth orbit satellite. Each of the reference stations determines first navigation observables based on the received signals and transmit information associated with the first navigation observables to the augmentation server. The augmentation server is configured to determine and distribute augmentation information to a receiver. The augmentation information is based on the received information associated with the first navigation observables, locations of the reference stations, and computational models. The distributed augmentation information is usable by the receiver to determine a high-precision position, velocity, and time solution for the receiver based on second navigation observables associated with the receiver.
Abstract:
Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.
Abstract:
A device implementing a system for estimating device location includes at least one processor configured to receive a first and second set of signals, each set corresponding to location data and being received based on a sampling interval. The at least one processor is configured to, for each sampling period defined by the sampling interval, obtain sensor data corresponding to device motion during the sampling period, determine an orientation of the device relative to that of the vehicle based on the sensor data, calculate a non-holonomic constraint based on the orientation of the device relative to that of the vehicle such that the non-holonomic constraint is iteratively updated, and estimate a device state based on the non-holonomic constraint.
Abstract:
Position, navigation and/or timing (PNT) solutions may be provided with levels of precision that have previously and conventionally been associated with carrier phase differential GPS (CDGPS) techniques that employ a fixed terrestrial reference station or with GPS PPP techniques that employ fixed terrestrial stations and corrections distribution networks of generally limited terrestrial coverage. Using techniques described herein, high-precision PNT solutions may be provided without resort to a generally proximate, terrestrial ground station having a fixed and precisely known position. Instead, techniques described herein utilize a carrier phase model and measurements from plural satellites (typically 4 or more) wherein at least one is a low earth orbiting (LEO) satellite. For an Iridium LEO solution, particular techniques are described that allow extraction of an Iridium carrier phase observables, notwithstanding TDMA gaps and random phase rotations and biases inherent in the transmitted signals.
Abstract:
Position, navigation and/or timing (PNT) solutions may be provided with levels of precision that have previously and conventionally been associated with carrier phase differential GPS (CDGPS) techniques that employ a fixed terrestrial reference station or with GPS PPP techniques that employ fixed terrestrial stations and corrections distribution networks of generally limited terrestrial coverage. Using techniques described herein, high-precision PNT solutions may be provided without resort to a generally proximate, terrestrial ground station having a fixed and precisely known position. Instead, techniques described herein utilize a carrier phase model and measurements from plural satellites (typically 4 or more) wherein at least one is a low earth orbiting (LEO) satellite. For an Iridium LEO solution, particular techniques are described that allow extraction of an Iridium carrier phase observables, notwithstanding TDMA gaps and random phase rotations and biases inherent in the transmitted signals.
Abstract:
Embodiments are disclosed for estimating time outdoors and in daylight based on ambient light, motion, and location sensing. In some embodiments, a method comprises detecting daylight based on an ambient light measurement, an estimated sun elevation angle and at least one confidence threshold; determining a motion or activity state of a user based on motion sensor data; determining an indoor or outdoor class based on the motion sensor data and the ambient light detections; determining user exposure time to daylight between, before or after ambient light detections, based on the motion or activity state, and the determined indoor or outdoor class; and storing or displaying the daylight time.
Abstract:
Techniques are described for improving driver efficiency. An example method can include a device accessing sparse location data indicative of one or more geographic locations along a route of the user device during a first time period. The route includes a starting location data point and an ending location data point. The device can access motion data collected by the sensors of the user device. The motion data can be collected by the sensors during the first time period. After a conclusion of the first time period, the device can generate, using the sparse location data and the motion data, a dense data set to reconstruct a route that includes the starting location data point and the ending location data point. The reconstructed route can include second dense location data and velocity data. The device can store the reconstructed route in a local memory of the user device.
Abstract:
A device implementing a system for estimating device position includes at least one processor configured to receive a first sensor measurement of a device at a first time, the first sensor measurement having a first variance in measurement error, and to receive a second sensor measurement of the device at a second time, the second sensor measurement having a second variance in measurement error. The at least one processor is further configured to determine a speed of the device based on at least one of the first or second sensor measurements, and adjust the second variance in measurement error based on the determined speed. The at least one processor is further configured to estimate a device position based at least in part on the first variance in measurement error and the adjusted second variance in measurement error.