Apple Inc.

Leland Stanford Junior University

David Benjamin Millman

Robert William Mayor

Brian Stephen Smith

Darin Tay

Joseph Ding-Jiu Huang

Vitali Lovich

Johan Olav Bergerengen

Abhinav R Patel

Jessica Noel Tsoong

Sebastian Thrun

Brent Michael Ledvina

Jasvinder Singh

Benjamin A Detwiler

David Stavens

Joseph Huang

Margaret H Tam

Anders M Holtsberg

Ashish Agarwal

Jahshan A Bhatti

Mithilesh Kumar

Ilya K Veygman

Vojislav Samsalovic

Rob Mayor

Rob Brumley

Ben Detwiler

Wuzhou Zhang

Techniques and systems for unlocking a first device based on signals transmitted between the first device and a second device are disclosed. A disclosed technique includes receiving, by a first device, at least one wireless signal from a second device; transmitting, by the first device, at least one wireless signal to the second device; determining, by the first device, transit times of the at least one received wireless signal and the at least one transmitted wireless signal; determining, by the first device, one or more range measurements between the first device and the second device based at least in part on the transit times; determining, by the first device, an unlock decision based at least in part on the one or more range measurements; and causing, by at least the first device, the first device to unlock if the unlock decision is positive.

Unlocking a device

An example method includes obtaining a plurality of data items. Each data item includes an indication of a particular location, an indication that a wireless signal from a first access point was observed at that location, and an indication of a time at which the wireless signal from the first access point was observed at that location. The method also includes determining a locational stability of the first access point based on the data items. Determining the locational stability of the first access point includes clustering the plurality of data items into one or more clusters based on the locations indicated in the plurality of data items, determining whether the N most recent data items are associated with a common cluster, and determining whether a time span between the N most recent data items exceeds a threshold period of time.

Managing moving wireless access points in wireless network positioning system

A method comprising: receiving a radio map of an indoor venue using survey data collected by a survey device positioned throughout the venue, the radio map including a boundary; receiving harvest data from a mobile device, wherein at least some of the harvest data are obtained by the mobile device while the mobile device is positioned at locations that are outside of the boundary; determining, based on the harvest data, a trajectory of the mobile device, wherein at least some of the trajectory resides outside of the boundary; identifying one or more locations on or proximate to the trajectory; and extending the radio map using the survey data and the one or more identified locations, wherein the extended radio map is defined at least in part by an extension of the boundary to encompass the one or more identified locations.

Extending a radio map

Methods, program products, and systems for using multiple sensors to determine a location fingerprint are described. A sampling device can measure RF signals detected at a train station of a transit system or a route of the transit system. The sampling device, or a location server receiving the measurements, can filter RF signal measurements using one or more readings from sensors coupled to the sampling device and that are different from RF receivers. The readings can be taken concurrently with the RF signal measurements. These readings, designated as motion cues, can include motion sensor readings, barometer readings, or magnetometer readings. Using the motion cues, the sampling device or location server can distinguish different platforms of a station of the transit system and different levels of the station, or filter out RF signal measurements that may have been inaccurate, e.g., as caused by disturbances from a train entering or leaving a station.

Location fingerprinting for a transit system

Coarse location estimation for mobile devices is disclosed for detecting mobile device presence at general locations of interest and switching operating modes and services for one or more location context aware applications. In some implementations, sensor data is received from a plurality of data sources at a location. For each data source, a first probability is estimated that the mobile device is at the location based on sensor data from the data sources. A second probability is estimated that the mobile device is not at the location based on sensor data from the data sources. The first and second estimated probabilities are statistically combined to generate a third estimated probability that the mobile device is at the location.

Coarse location estimation for mobile devices

Embodiments are disclosed for compressing radio maps of fingerprint-based positioning systems using different compression models. In an embodiment, a method comprises: receiving, by a computing device, access point (AP) data from a plurality of mobile devices operating in a geographic region, the AP data including signal strength measurements of AP signals received at a plurality of reference locations in the geographic region and uncertainty measurements associated with the signal strength measurements; determining a level of accuracy with the first compression model; responsive to the determining, selecting one of the first compression model or a second compression model to compress the AP data, the second compression model being different than the first compression model; compressing the AP data using the selected compression model; and responsive to a request from a mobile device operating in the geographic region, sending a data packet including the compressed AP data to the mobile device.

Compressing radio maps using different compression models

In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that is produces excellent results in practice.

Method and system for signal-based localization

Embodiments are disclosed for compressing radio maps of fingerprint-based positioning systems. In an embodiment, a method comprises: receiving access point (AP) data from a plurality of mobile devices operating in a geographic region, the AP data including signal strength measurements of AP signals received at a plurality of reference locations in the geographic region; filtering the AP data to remove outlier AP data; fitting a surface to the AP data; projecting AP data at surface control points onto a two-dimensional image grid; determining a boundary surrounding locations of the AP data at the surface control points; encoding the boundary; encoding the AP data at the surface control points included within the boundary; generating compressed radio maps from the encoded AP data; and responsive to a request from a mobile device operating in the geographic region, sending a data packet including the compressed radio maps to the mobile device.

Compressing radio maps

In some implementations, a server device can improve Wi-Fi based location availability for sparse signal locations by augmenting Wi-Fi access point location data with additional Wi-Fi scan data that was collected near the sparse signal locations. The server can receive Wi-Fi access point scans from client devices, determine the locations of the Wi-Fi access points, and generate Wi-Fi access point tiles that include data describing the locations of access points in the geographic areas corresponding to the Wi-Fi access point tiles. After the tiles are generated, the server device can augment the access point tiles by inserting additional Wi-Fi access point scan information into the access point tiles near identified sparse signal locations. The server device can deliver the augmented access point tile data to a client device. The client device can determine the current location of the client device based on the augmented access point tile data.

Wi-Fi based location availability for sparse signal locations

Methods, systems, and computer program products for correcting in-venue location estimation using structural information are described. A mobile device can use wireless location technologies and dead reckoning to determine an estimated location of the mobile device in a venue. The mobile device can compare the estimated location with a map of the venue. Upon determining that the estimated location conflicts with a structural constraint, the mobile device can adjust the location estimation using the structural information. Adjusting the location estimation can include adjusting a statistical filter that provides estimation of the location and changing a heading of the mobile device used in the dead reckoning.

Correcting in-venue location estimation using structural information

In an embodiment of the present invention, a GraphSLAM-like algorithm for signal strength SLAM is presented. This algorithm as an embodiment of the present invention shares many of the benefits of Gaussian processes yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O(N) operations per iteration. In the present disclosure, an algorithm according to an embodiment of the present invention is compared to a laser-SLAM ground truth, showing that it produces excellent results in practice.

Methods, systems, and computer program product for generating location fingerprint data for a venue are described. A sampling device surveying a venue can move inside the venue. While the sampling device moves, the sampling device can record environmental readings including, for example, strengths of signals from multiple radio signal sources. The sampling device can take the recording at fixed or various time intervals. Meanwhile, the sampling device can determine, based on a starting point and motion sensor readings, an estimated location of the mobile device for each time the sampling device takes the recordings. The sampling device can display a motion path of the estimated locations and a user interface item for receiving a user input for correcting the estimated locations. The sample device can tag the recorded environmental readings with the corrected locations, and submit the tagged readings to a server for determining a location fingerprint for the venue.

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Mountain View, CA, United States of America

David Benjamin Millman