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    • 2. 发明授权
    • Indoor localization of mobile device using labels
    • 使用标签的移动设备的室内本地化
    • US08538442B1
    • 2013-09-17
    • US13160716
    • 2011-06-15
    • Scott EttingerMohammed Waleed KadousAndrew Lookingbill
    • Scott EttingerMohammed Waleed KadousAndrew Lookingbill
    • H04W40/00
    • H04W40/244G01S5/0278
    • To localize a mobile device in an indoor area, labels are positioned at locations throughout the indoor area and signal strength is recorded at the label positions from different wireless network access points that are available in the indoor area. As a user traverses the indoor area with the mobile device, individual label identifiers are entered into the mobile device and the strength of the wireless network signals is recorded at each label position. The sampled wireless network signal strength and the corresponding label identifiers and positions are recorded to generate a mapping that identifies wireless network signal strength at each of the different label positions in the indoor area. The mapping may then be accessed to identify the locations of other mobile device users that subsequently enter the indoor area based on the wireless network signal strength from the different access points detected by the other mobile devices.
    • 为了将移动设备本地化在室内区域中,标签位于室内区域的位置处,并且信号强度被记录在室内区域中可用的不同无线网络接入点的标签位置处。 当用户通过移动设备遍历室内区域时,将各个标签标识符输入到移动设备中,并且在每个标签位置记录无线网络信号的强度。 记录采样的无线网络信号强度和对应的标签标识符和位置,以产生识别室内区域中每个不同标签位置处的无线网络信号强度的映射。 然后可以访问映射,以便基于来自其他移动设备检测到的不同接入点的无线网络信号强度来识别随后进入室内区域的其他移动设备用户的位置。
    • 5. 发明授权
    • Predicting parking availability
    • 预测停车可用性
    • US08484151B1
    • 2013-07-09
    • US12981278
    • 2010-12-29
    • Andrew Lookingbill
    • Andrew Lookingbill
    • G06F17/00G06N5/02
    • G08G1/0129G01C21/3685G06N99/005G06Q10/04G08G1/012G08G1/0141G08G1/144G08G1/147
    • The parking availability for a geographic area is predicted using a parking availability model. The geographic population density for the geographic area is first predicted. The predicted geographic population density is then applied to the parking availability model to produce a prediction of the parking availability for the geographic area. The parking availability model comprises a function that relates predicted geographic population densities with parking availabilities for a geographic area. The predicted parking availability for the geographic area is stored in a computer-readable storage medium. The parking availability predictions may be displayed as a layer within a map, and may be produced for a specifically requested geographic area, or a general geographic area.
    • 使用停车可用性模型预测地理区域的停车可用性。 首先预测地理区域的地理人口密度。 然后将预测的地理人口密度应用于停车可用性模型,以产生对于地理区域的停车可用性的预测。 停车可用性模型包括将预测的地理人口密度与地理区域的停车可用性相关联的功能。 预测的地理区域的停车可用性被存储在计算机可读存储介质中。 停车可用性预测可以被显示为地图内的层,并且可以针对特定请求的地理区域或一般地理区域来生成。
    • 6. 发明授权
    • Predicting geographic population density
    • 预测地理人口密度
    • US08478289B1
    • 2013-07-02
    • US12791368
    • 2010-06-01
    • Andrew LookingbillSebastian Thrun
    • Andrew LookingbillSebastian Thrun
    • H04W24/00
    • H04W4/021H04W16/22
    • The population density for a geographic area is predicted using a Markov Random Field (MRF) model. A MRF model is defined for estimating a number of mobile devices being used within a geographic area. The MRF model includes a set of rules describing how to use current data describing mobile devices currently observed in the area, and historical data describing mobile devices historically observed in the area to produce the estimate. Values of weight parameters in the MRF model are learned using the historical data. The current and historical data are applied to the MRF model having the learned weight parameters, and cost minimization is used to estimate of the number of mobile devices currently being used within the area. This estimate is used to predict the population density for the area. The predicted population density can then be used to provide location-based services.
    • 使用马尔科夫随机场(MRF)模型预测地理区域的人口密度。 定义MRF模型以估计在地理区域内正在使用的移动设备的数量。 MRF模型包括一组规则,描述如何使用当前在该区域观察到的移动设备的当前数据,以及描述在该区域中历史观察到的移动设备以产生估计的历史数据。 使用历史数据学习MRF模型中权重参数的值。 当前和历史数据被应用于具有所学习的权重参数的MRF模型,并且使用成本最小化来估计当前在该区域内正在使用的移动设备的数量。 这个估计用于预测该地区的人口密度。 然后可以使用预测的人口密度来提供基于位置的服务。
    • 10. 发明申请
    • QUALITY CONTROL OF MAPPING DATA
    • 映射数据的质量控制
    • US20120290636A1
    • 2012-11-15
    • US13105361
    • 2011-05-11
    • Mohammed Waleed KadousAndrew Lookingbill
    • Mohammed Waleed KadousAndrew Lookingbill
    • G06F15/16
    • G01S1/72G01C21/206G01C21/32G09B29/10G09B29/106H04W4/043
    • Aspects of the disclosure relate to quality control of survey data used to generate and or supplement map information. A device may be walked through an indoor space in order to collect survey data (accelerometer, gyroscope, wireless network identifiers, etc.). The survey data is then transmitted to a server for further processing to identify the path (or the various locations) of the device in the indoor space. The path may be determined by referring to a map of the indoor location and a localization algorithm, for example, a particle filter or least squares optimizer. The path may be compared to other survey data and paths from the same indoor space as well as the map in order to provide an estimate of the quality of the localization produced for the survey data. Low quality survey data may be flagged for further review or used to make changes to the map.
    • 本公开的方面涉及用于生成和/或补充地图信息的勘测数据的质量控制。 为了收集测量数据(加速度计,陀螺仪,无线网络标识符等),设备可以通过室内空间进行。 然后将调查数据发送到服务器进行进一步处理以识别室内空间中设备的路径(或各种位置)。 可以通过参考室内位置的映射和定位算法(例如,粒子滤波器或最小二乘法优化器)来确定路径。 该路径可以与来自相同室内空间以及地图的其他测量数据和路径进行比较,以便提供对测量数据产生的定位质量的估计。 低质量的调查数据可能被标记为进一步审查或用于对地图进行更改。