Google Inc.

Deutsches Zentrum Fur Luft-und Raumfahrt E.v.

Bruno, Luigi

Luigi Bruno

Mohammed Khider

Etienne Le Grand

Kevin Oishi

Michael Sprague

Wei Wang

Brian Patrick Williams

Patrick Robertson

Maria Jesus Garcia Puyol

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

Systems and methods for generating geographic data for map elements based on surfel data and motion data associated with a geographic region are disclosed. A computing system can obtain motion data indicative of movement of at least one object in a geographic region represented by a map. The computing system can additionally obtain surfel data indicative of one or more surface elements associated with a surface of a structure in the geographic region represented by the map. The computing system can identify an entrance of the structure based at least in part on a correlation of the surfel data and the motion data. The computing system can generate geographic data indicative of a geographic location of the entrance of the structure.

Map feature identification using motion data and surfel data

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

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.

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Mountain View, CA, United States of America

Luigi Bruno