Google Inc.

Mohammed Khider

Etienne Le Grand

Frank Van Diggelen

Luigi Bruno

Brian Patrick Williams

Patrick Robertson

Arastoo Zabolestani Moradi

Kevin William Watts

Imad Jean Fattouch

Zifei Tong

David Orendorff

Wei Wang

Imad Fattouch

David William Earle Orendorff

Stefano Maggiolo

Maria Jesus Garcia Puyol

Guoyu Fu

This document describes a client-server approach for indoor-outdoor detection of an electronic device, and associated systems and methods. A server () collects crowdsourced information () from devices that detected a plurality of access points. An electronic device (), performing a wire-less-network scan, detects access points () within range and detects sensor data () from other sensors (). The electronic device () transmits such information to the server (). The server accesses the crowdsourced information () to determine, per access point () detected in the scan, a percentage of total detections of the access point that are accompanied by a GPS signal of a device that detected the access point and an RSS value below which no such GPS signal accompanies the detections. The percentage and the RSS value enable a determination of a probability indicating whether the electronic device () is located outdoors, lightly indoors, or deep indoors, which enables the electronic device to trigger a corresponding function.

Client-server approach for indoor-outdoor detection of an electronic device, and associated systems and methods

A method of processing signal paths includes receiving an estimated location for a GNSS receiver in an environment. The method also includes generating a plurality of candidate positions about the estimated location where each candidate position corresponds to a possible actual location of the GNSS receiver. The method further includes, for each available satellite at each candidate position, modeling a plurality of candidate signal paths by ray-launching a raster map of geographical data. Here, the plurality of candidate signal paths includes one or more reflected signal paths. At each candidate position, the method also includes comparing, the plurality of candidate signal paths modeled for each available satellite at the respective candidate position to measured GNSS signal data from the GNSS receiver and generating a likelihood that the respective candidate position includes the actual location of the GNSS receiver based on the comparison.

GNSS signal modeling

A method of processing signal paths includes receiving an estimated location for a GNSS receiver in an environment. The method also includes generating a plurality of candidate positions about the estimated location where each candidate position corresponds to a possible actual location of the GNSS receiver. The method further includes, for each available satellite at each candidate position, modeling a plurality of candidate signal paths by ray-launching a raster map of geographical data Here, the plurality of candidate signal paths includes one or more reflected signal paths. At each candidate position, the method also includes comparing, the plurality of candidate signal paths modeled for each available satellite at the respective candidate position to measured GNSS signal data from the GNSS receiver and generating a likelihood that the respective candidate position includes the actual location of the GNSS receiver based on the comparison.

A method of localization using bearing from environmental features includes receiving an estimated location of a global navigation satellite system (GNSS) receiver associated with a user and a corresponding bearing for the GNSS receiver. The method also includes identifying one or more environmental features about the estimated location of the GNSS receiver. The method further includes determining whether an orientation of a respective environmental feature of the one or more environmental features correlates to the corresponding bearing for the GNSS receiver. When the orientation of the respective environmental feature correlates to the corresponding bearing for the GNSS receiver, the method includes generating an updated bearing for the GNSS receiver or locational system that matches the orientation of the respective environmental feature.

Localization using bearing from environmental features

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

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

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.

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 this example, the functions include receiving sensor data that is collected by one or more sensors of a device over one or more locations and over a time period. Further, in the present example, the functions also include determining location estimates of the device by performing filtering of the sensor data to determine offsets for a least one sensor providing sensor data. The filtering is an iterative process of filtering control input data to determine the sensor bias based on data from a second sensor of the at least two sensors and adjusting the set of sensor data based on the determined bias.

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Mountain View, CA, United States of America

Mohammed Khider