Google Inc.

Etienne Le Grand

Brian Patrick Williams

Mohammed Khider

Mohammed Waleed Kadous

Luigi Bruno

Brian John Julian

Patrick Robertson

Michael Andrew Sipe

Marc Stogaitis

David Nicholas Christie

Mathias Marc Agopian

Maria Jesus Garcia Puyol

Examples describe systems and methods for iteratively determining a signal strength map for a wireless access point (AP) aligned to position coordinates. An example method includes receiving logs of data from devices. For a plurality of iterations, the method includes selecting a set of logs of data having an amount of GPS being less than a given amount of GPS in a previously selected set, determining estimates of signal strength maps for the wireless AP aligned to position coordinates based on the selected set and on given signal strength maps due to a previous iteration, and performing a simultaneous localization and mapping (SLAM) optimization of the possible locations of the wireless AP based on the given signal strength maps and the estimates of the signal strength maps. Based on the iterative optimizations, an output signal strength map is provided for the wireless AP aligned to position coordinates.

Use of offline algorithm to determine location from previous sensor data when location is requested

Examples include systems and methods for decomposition of error components between angular, forward, and sideways errors in estimated positions of a computing device. One method includes determining an estimation of a current position of the computing device based on a previous position of the computing device, an estimated speed over an elapsed time, and a direction of travel of the computing device, determining a forward, sideways, and orientation change error component of the estimation of the current position of the computing device, determining a weight to apply to the forward, sideways, and orientation change error components based on average observed movement of the computing device, and using the weighted forward, sideways, and orientation change error components as constraints for determination of an updated estimation of the current position of the computing device.

Decomposition of error components between angular, forward, and sideways errors in estimated positions of a computing device

Positioning mobile devices in a three-dimensional space includes receiving multiple traces, each trace corresponding to a sequence of atmospheric pressure readings from a respective mobile device, receiving indications of signals received by the mobile devices from signal sources concurrently with the atmospheric pressure readings, generate similarity metrics for the multiple traces using the indications of other signals received by the mobile devices, the similarity metrics being indicative of associations between the signal sources and the atmospheric pressure readings, and determine estimated changes in elevation over time for the multiple traces using the generated similarity metrics.

Determining position of a device in three-dimensional space and corresponding calibration techniques

Traces collected by multiple portable devices moving within a geographic area that includes an indoor region, each of the traces including measurements of wireless signals sources at different times by a same device, and at least some of the traces including pseudorange measurements related to distances to respective satellites. Location estimates for the portable devices and the signal sources are generated using graph-based SLAM optimization of the location estimates. More particularly, constraints for the pseudorange measurements are generated and applied for the pseudorange measurements in graph-based SLAM optimization.

Systems and methods for graph-based localization and mapping

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.

Determining quality of a location-determination algorithm associated with a mobile device by processing a log of sensor data

A system includes one or more processors, and data storage configured to store instructions that, when executed by the one or more processors, cause the system to perform functions. In one example, the functions include receiving logs of data, wherein respective data in the received logs of data are collected by one or more sensors of a device over one or more locations and over a time period. In the present example, the functions also include determining location estimates of the device by performing a simultaneous localization and mapping (SLAM) optimization of the location estimates using barometer data and GPS elevational data available in the logs of data, wherein the location estimates indicate elevational locations of the device over the time period.

Construction of a surface of best GPS visibility from passive traces using SLAM for horizontal localization and GPS readings and barometer readings for elevation estimation

Examples include systems and methods for adjusting interrupt timestamps associated with sensor measurement data. One method includes receiving from a sensor an interrupt signal, storing an interrupt timestamp corresponding to the interrupt signal, and determining expected sampling rates associated with the sensor. The method also includes determining a first minimal latency timestamp based on a comparison of one or more previously stored interrupt timestamps and the expected sampling rates, and determining a second minimal latency timestamp based on the interrupt timestamp, one or more previously stored interrupt timestamps, and the expected sampling rates. The method also includes determining an estimated sampling rate of the sensor based on the first and second minimal latency timestamps, and determining an adjusted interrupt timestamp corresponding to the interrupt signal based on the estimated sampling rate and the interrupt timestamp.

Adjustment of interrupt timestamps of signals from a sensor based on an estimated sampling rate of the sensor

Traces are collected by multiple portable devices moving with an area that includes an indoor region, with each of the traces including measurements of wireless signals at different times, including measurements of wireless signals from signal sources disposed within the area. A motion map for the geographic area is constructed by determining, for each of the cells that make the motion map, respective probabilities of moving in various directions relative to each cell. Location estimates for the portable devices and the signal sources are generated using graph-based SLAM optimization of the location estimates. The graph-based SLAM optimization includes determining to which of the cells of the motion map the location estimate corresponds and applying the measurements of wireless signals sources and the set of probabilities of the cells as a first constraint and a second constraint, respectively, in the graph-based SLAM optimization.

In one example, a method includes determining, by a first motion module of a computing device and based on first motion data measured by a first motion sensor at a first time, that the mobile computing device has moved, wherein a display operatively coupled to the computing device is deactivated at the first time; responsive to determining that the computing device has moved, activating a second motion module; determining, by the second motion module, second motion data measured by a second motion sensor, wherein determining the second motion data uses a greater quantity of power than determining the first motion data; determining a statistic of a group of statistics based on the second motion data; and responsive to determining that at least one of the group of statistics satisfies a threshold, activating the display.

Buttonless display activation

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes modeling signals of wireless signal emitters. A computing device can determine first and second trained Gaussian processes. The respective first and second Gaussian processes can be based on first and second hyperparameter values related to first and second wireless signal emitters. The computing device can determine first and second sets of comparison hyperparameter values of the respective first and second hyperparameter values, and then determine whether the first and second sets of comparison hyperparameter values are within one or more threshold values. After determining that the first and second sets of comparison hyperparameter values are within the threshold(s), the computing device can determine the first and second Gaussian processes are dependent and then provide an estimated-location output based on a representative Gaussian process based on the first and the second Gaussian processes.

Gaussian process-based approach for identifying correlation between wireless signals

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on trained Gaussian processes. A computing device can determine trained Gaussian processes related to wireless network signal strengths, where a particular trained Gaussian process is associated with one or more hyperparameters. The computing device can designate one or more hyperparameters. The computing device can determine a hyperparameter histogram for values of the designated hyperparameters of the trained Gaussian processes. The computing device can determine a candidate Gaussian process associated with one or more candidate hyperparameter value for the designated hyperparameters. The computing device can determine whether the candidate hyperparameter values are valid based on the hyperparameter histogram. The computing device can, after determining that the candidate hyperparameter values are valid, add the candidate Gaussian process to the trained Gaussian processes. The computing device can provide an estimated location output based on the trained Gaussian processes.

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Mountain View, CA, United States of America

Etienne Le Grand