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公开(公告)号：US09838847B2
公开(公告)日：2017-12-05
申请号：US14843955
申请日：2015-09-02
Applicant: Google Inc.
Inventor： Brian John Julian , Etienne Le Grand , Brian Patrick Williams
CPC classification number: H04W4/029 , H04L41/142 , H04L43/08 , H04W4/023
Abstract: 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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22.发明授权Computational complexity reduction of training wireless strength-based probabilistic models from big data 有权从大数据中训练基于无线强度的概率模型的计算复杂度降低公开(公告)号：US09591454B2
公开(公告)日：2017-03-07
申请号：US14843935
申请日：2015-09-02
Applicant: Google Inc.
Inventor： Brian John Julian , Etienne Le Grand , Brian Patrick Williams
Abstract: Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on signal strength measurements. A computing device can receive a particular signal strength measurement, which can include a wireless-signal-emitter (WSE) identifier and a signal strength value and can be associated with a measurement location. The computing device can determine one or more bins; each bin including statistics for WSEs and associated with a bin location. The statistics can include mean and standard deviation values. The computing device can: determine a particular bin whose bin location is associated with the measurement location for the particular signal strength measurement, determine particular statistics of the particular bin associated with a wireless signal emitter identified by the WSE identifier of the particular signal strength measurement, and update the particular statistics based on the signal strength value. The computing device can provide an estimated location output based on the bins.
Abstract translation: 公开了用于提供输出的装置和方法; 例如，基于信号强度测量的位置估计。 计算设备可以接收特定的信号强度测量，其可以包括无线信号 - 发射器（WSE）标识符和信号强度值，并且可以与测量位置相关联。 计算设备可以确定一个或多个箱; 每个bin包括WSE的统计信息并与bin位置相关联。 统计数据可以包括平均值和标准偏差值。 计算设备可以：确定其仓位置与特定信号强度测量的测量位置相关联的特定仓，确定与由特定信号强度测量的WSE标识符识别的无线信号发射器相关联的特定仓的特定统计， 并基于信号强度值来更新特定统计信息。 计算设备可以基于箱提供估计的位置输出。
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23.发明授权Use of a trained classifier to determine if a pair of wireless scans came from the same location 有权使用训练有素的分类器来确定一对无线扫描是否来自同一位置公开(公告)号：US09571977B2
公开(公告)日：2017-02-14
申请号：US15018936
申请日：2016-02-09
Applicant: Google Inc.
Inventor： Mohammed Waleed Kadous , Marc Stogaitis , Brian Patrick Williams , Etienne Le Grand
Abstract: The present disclosure describes methods, systems, and apparatuses for determining the likelihood that two wireless scans of a mobile computing device were performed in the same location. The likelihood is determined by scanning for wireless networks with a computing device. The scanning includes a receiving a plurality of network attributes for each wireless networks within the range of the mobile computing device. Further, the likelihood is determined by comparing the plurality of network attributes from the scanning with a reference set of network attributes. The comparing of network attributes is used to determine an attribute comparison. Finally, the likelihood between a position associated with the reference set of network attributes and the computing device, based on the attribute comparison, determines a position associated with the network.
Abstract translation: 本公开描述了用于确定在相同位置执行移动计算设备的两次无线扫描的可能性的方法，系统和装置。 通过用计算设备扫描无线网络来确定可能性。 扫描包括为移动计算设备的范围内的每个无线网络接收多个网络属性。 此外，通过将来自扫描的多个网络属性与网络属性的参考集进行比较来确定似然性。 网络属性的比较用于确定属性比较。 最后，基于属性比较，与参考网络属性集合相关联的位置与计算设备之间的可能性确定与网络相关联的位置。
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24.发明授权Use of a trained classifier to predict distance based on a pair of wireless scans 有权使用训练有素的分类器来基于一对无线扫描来预测距离公开(公告)号：US09351117B2
公开(公告)日：2016-05-24
申请号：US13972738
申请日：2013-08-21
Applicant: Google Inc.
Inventor： Mohammed Waleed Kadous , Brian Patrick Williams , Etienne Le Grand
CPC classification number: H04W4/023
Abstract: The present disclosure describes methods, systems, and apparatuses for determining the distance between two wireless scans of a mobile computing device. The distance is determined by scanning for wireless networks with a computing device. The scanning includes a receiving a plurality of network attributes for each wireless networks within the range of the mobile computing device. Further, the distance is determined by comparing the plurality of network attributes from the scanning with a reference set of network attributes. The comparing of network attributes is used to determine an attribute comparison. Finally, the distance between a position associated with the reference set of network attributes and the computing device, based on the attribute comparison, determines a position associated with the network.
Abstract translation: 本公开描述了用于确定移动计算设备的两次无线扫描之间的距离的方法，系统和装置。 通过用计算设备扫描无线网络来确定距离。 扫描包括为移动计算设备的范围内的每个无线网络接收多个网络属性。 此外，通过将来自扫描的多个网络属性与网络属性的参考集进行比较来确定距离。 网络属性的比较用于确定属性比较。 最后，基于属性比较，与参考网络属性相关联的位置与计算设备之间的距离确定与网络相关联的位置。
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25.发明申请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 审中-公开使用SLAM进行水平定位和GPS读数和仰角估计的晴雨表读数，从被动轨迹构建最佳GPS可见度曲面公开(公告)号：US20160033266A1
公开(公告)日：2016-02-04
申请号：US14694240
申请日：2015-04-23
Applicant: Google Inc.
Inventor： Etienne Le Grand , Mohammed Khider
CPC classification number: G01L19/0092 , G01S5/0263 , G01S19/01 , G01S19/48
Abstract: 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.
Abstract translation: 系统包括一个或多个处理器，以及数据存储器，被配置为存储当由一个或多个处理器执行时使系统执行功能的指令。 在一个示例中，功能包括接收数据的日志，其中数据的接收日志中的相应数据由设备的一个或多个传感器在一个或多个位置上以及一段时间段内收集。 在本示例中，功能还包括通过使用气压计数据和在数据日志中可用的GPS高程数据执行位置估计的同时定位和映射（SLAM）优化来确定设备的位置估计，其中位置估计指示高程 时间段内设备的位置。
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Patent Agency Ranking
26.发明申请Determining Quality of a Location-Determination Algorithm Associated with a Mobile Device by Processing a Log of Sensor Data 审中-公开通过处理传感器数据的日志来确定与移动设备相关联的位置确定算法的质量公开(公告)号：US20160033265A1
公开(公告)日：2016-02-04
申请号：US14447517
申请日：2014-07-30
Applicant: Google Inc.
Inventor： Etienne Le Grand , David Nicholas Christie
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 translation: 描述了用于评估移动设备的位置确定算法的质量的方法和系统。 示例性方法可以包括接收传感器数据的日志，所述传感器数据可以包括在一段时间内由移动设备的给定传感器输出的传感器值，以及在该时间段内的至少一个相应时间点的至少一个位置估计。 然后，一个或多个处理器可以使用传感器值，在该时间段内确定包括移动设备的多个计算的地面真相位置的估计轨迹。 此外，该方法可以包括确定给定位置估计与多个计算的地面真相位置的计算的地面真相位置之间的差。 并且该方法可以包括提供指示所确定的差是否满足预定阈值的输出。
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公开(公告)号：US09159294B2
公开(公告)日：2015-10-13
申请号：US14230880
申请日：2014-03-31
Applicant: Google Inc.
Inventor： Etienne Le Grand , Michael Andrew Sipe
CPC classification number: G06F3/017 , G06F1/3215 , G06F1/3265 , G06F1/3278 , G06F1/3287 , G06F3/01 , G09G5/003 , G09G2330/021 , Y02D10/153 , Y02D10/157
Abstract: 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.
Abstract translation: 在一个示例中，一种方法包括由计算设备的第一运动模块基于第一运动传感器在第一时间测量的第一运动数据来确定移动计算设备已经移动，其中显示器可操作地耦合到 第一次停用计算设备; 响应于确定所述计算设备已经移动，激活第二运动模块; 由所述第二运动模块确定由第二运动传感器测量的第二运动数据，其中确定所述第二运动数据使用比确定所述第一运动数据更大的功率量; 基于所述第二运动数据确定一组统计的统计量; 并且响应于确定所述一组统计信息中的至少一个满足阈值，激活所述显示。
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28.发明申请Decomposition of Error Components Between Angular, Forward, and Sideways Errors in Estimated Positions of a Computing Device 有权在计算设备的估计位置中的角度，前向和侧向错误之间的误差分量的分解公开(公告)号：US20150226577A1
公开(公告)日：2015-08-13
申请号：US14176241
申请日：2014-02-10
Applicant: GOOGLE INC.
Inventor： Etienne Le Grand , Brian Patrick Williams
CPC classification number: G01C25/00 , G01C19/00 , G01C21/12 , G01C22/006 , G01C25/005 , G01S5/02 , G01S19/13 , G01S19/49 , H04W4/026 , H04W4/027 , H04W4/029
Abstract: 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.
Abstract translation: 示例包括用于在计算设备的估计位置的角度，前向和侧向误差之间分解误差分量的系统和方法。 一种方法包括基于计算设备的先前位置，经过的时间的估计速度和计算设备的行进方向来确定计算设备的当前位置的估计，确定前向，侧向和定向 改变计算装置的当前位置的估计的误差分量，基于计算装置的平均观察运动确定应用于向前，侧向和方向改变误差分量的权重，以及使用加权的向前，侧向和 取向变化误差分量作为用于确定计算设备的当前位置的更新估计的约束。
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29.发明申请Methods and Systems for Determining Signal Strength Maps for Wireless Access Points Robust to Measurement Counts 有权用于确定无线接入点的信号强度图的方法和系统可靠地测量计数公开(公告)号：US20150222372A1
公开(公告)日：2015-08-06
申请号：US14173052
申请日：2014-02-05
Applicant: Google Inc.
Inventor： Etienne Le Grand , Brian Patrick Williams
CPC classification number: H04B17/24 , H04B17/318 , H04W24/04 , H04W24/08 , H04W24/10
Abstract: Examples herein include methods and systems for determining signal strength maps for wireless access points robust to measurement counts. An example method comprises receiving data related to RSSI for a wireless AP for a plurality of locations of an area, and determining an intermediary signal strength map for the wireless AP based on the received data related to the RSSI for the wireless AP. The method also includes associating the intermediary signal strength map to a regularized signal strength map for the wireless AP that is based on a diffusion mapping model of signal strength. A given partition of the regularized signal strength map is linked to one partition of the intermediary signal strength map. The method also includes providing an output signal strength map for the wireless AP including values of the regularized signal strength map modified based on values of the intermediary signal strength map.
Abstract translation: 本文的示例包括用于确定对测量计数坚固的无线接入点的信号强度图的方法和系统。 一种示例性方法包括接收关于区域的多个位置的无线AP的与RSSI有关的数据，以及基于与无线AP的RSSI相关的接收数据，确定无线AP的中间信号强度图。 该方法还包括将中间信号强度图与基于信号强度的扩散映射模型的无线AP的正则化信号强度图相关联。 正则化信号强度图的给定分区链接到中间信号强度图的一个分区。 该方法还包括提供无线AP的输出信号强度图，包括基于中间信号强度图的值修改的正则化信号强度图的值。
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公开(公告)号：US10075818B2
公开(公告)日：2018-09-11
申请号：US15703779
申请日：2017-09-13
Applicant: GOOGLE INC.
Inventor： Etienne Le Grand , Mohammed Khider , Luigi Bruno
Abstract: 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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