Abstract: Methods and systems for evaluating the quality of a location-determination algorithm of a mobile device are described. An example method may involve receiving a log of sensor data that may include sensor values output by given sensors of a mobile device over a time period, and at least one location estimate for at least one respective point in time within the time period. One or more processors may then determine, using the sensor values, an estimated trajectory that includes a plurality of computed ground-truth locations of the mobile device over the time period. Further, the method may involve determining a difference between a given location estimate and a computed ground-truth location of the plurality of computed ground-truth locations. And the method may involve providing an output indicative of whether the determined difference satisfies a predetermined threshold.

Abstract: Examples describe methods, computing devices, and systems for using location provider (LP) processes to accurately identify a proximate location of a computing device, while reducing the power consumption of the computing device. An example method may include executing, on a computing device, both a first LP process and a second LP process simultaneously for at least part of a time period to determine outputs indicative of locations of the computing device for the time period. Further, the method may include determining a motion model of the computing device that indicates expected locations of the computing device. Yet further, the method may include making a comparison of the outputs to the motion model for the time period. In addition, the method may include disabling one of the LP processes based on the comparison.

Abstract: Examples describe methods, computing devices, and systems for using location provider (LP) processes to accurately identify a proximate location of a computing device, while reducing the power consumption of the computing device. An example method may include executing, on a computing device, both a first LP process and a second LP process simultaneously for at least part of a time period to determine outputs indicative of locations of the computing device for the time period. Further, the method may include determining a motion model of the computing device that indicates expected locations of the computing device. Yet further, the method may include making a comparison of the outputs to the motion model for the time period. In addition, the method may include disabling one of the LP processes based on the comparison.

Abstract: Examples describe methods, computing devices, and systems for using location provider (LP) processes to accurately identify a proximate location of a computing device, while reducing the power consumption of the computing device. An example method may include executing, on a computing device, both a first LP process and a second LP process simultaneously for at least part of a time period to determine outputs indicative of locations of the computing device for the time period. Further, the method may include determining a motion model of the computing device that indicates expected locations of the computing device. Yet further, the method may include making a comparison of the outputs to the motion model for the time period. In addition, the method may include disabling one of the LP processes based on the comparison.

Abstract: Methods and systems for evaluating the quality of a location-determination algorithm of a mobile device are described. An example method may involve receiving a log of sensor data that may include sensor values output by given sensors of a mobile device over a time period, and at least one location estimate for at least one respective point in time within the time period. One or more processors may then determine, using the sensor values, an estimated trajectory that includes a plurality of computed ground-truth locations of the mobile device over the time period. Further, the method may involve determining a difference between a given location estimate and a computed ground-truth location of the plurality of computed ground-truth locations. And the method may involve providing an output indicative of whether the determined difference satisfies a predetermined threshold.

Abstract: Examples describe methods, computing devices, and systems for using location provider (LP) processes to accurately identify a proximate location of a computing device, while reducing the power consumption of the computing device. An example method may include executing, on a computing device, both a first LP process and a second LP process simultaneously for at least part of a time period to determine outputs indicative of locations of the computing device for the time period. Further, the method may include determining a motion model of the computing device that indicates expected locations of the computing device. Yet further, the method may include making a comparison of the outputs to the motion model for the time period. In addition, the method may include disabling one of the LP processes based on the comparison.

Abstract: Methods and systems for enabling a mobile device to batch sensor data based on events associated with the mobile device and without utilizing one or more main application processors are disclosed. In one example, a system is provided. The system may include one or more sensors, a memory, one or more main application processors, and one or more sensor processors. The one or more main application processors may be configured to, based on an event associated with the mobile device, send data indicating a request for sensor data to the one or more sensor processors. In response, the one or more sensor processors may be configured to determine sensor data using the one or more sensors while the one or more main application processors of the mobile device is in the inactive state. Once the sensor data has been determined, the one or more sensor processors may store the sensor data into one or more first in, first out (FIFO) queues in the memory.

Abstract: Methods and systems for determining a location of a mobile device using a multi-modal Kalman filter are described. According to an example method, a mobile device may maintain multiple approximations of a location of a mobile device. Each approximation includes an estimated geographic location of the mobile device that is determined by filtering a respective subset of location estimates received by the mobile device using a respective Kalman filter, and one of the multiple approximations is designated as an active approximation. The method also involves receiving data indicating an estimate of a geographic location of the mobile device and, based on a distance between the estimate of the geographic location and a given approximation of the multiple approximations, updating the given approximation using the estimate of the geographic location. Additionally, the method involves providing for display a visual indication of an estimated geographic location associated with the active approximation.

Abstract: Method and system for generating synthetic panchromatic imagery. A method for making a multi-spectral image includes capturing a panchromatic image of an imaging target. Additionally, the method includes capturing at least a first spectral image of the imaging target in a first spectral bandwidth, and capturing at least a second spectral image of the imaging target in a second spectral bandwidth. Also, the method includes determining at least a first spectral weight and a second spectral weight for the first spectral image and the second spectral image respectively. Additionally, the method includes generating a synthetic panchromatic image, determining a registration offset between the synthetic panchromatic image and the captured panchromatic image, warping the first spectral image and the second spectral image, and generating a multi-spectral image.