Methods for improved heading estimation

    公开(公告)号:US09739635B2

    公开(公告)日:2017-08-22

    申请号:US13915998

    申请日:2013-06-12

    CPC classification number: G01C25/00 G01C17/38

    Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

    Crowd sourced mapping with robust structural features
    23.
    发明授权
    Crowd sourced mapping with robust structural features 有权
    拥有强大结构特征的人群采集映射

    公开(公告)号:US09395190B1

    公开(公告)日:2016-07-19

    申请号:US14714212

    申请日:2015-05-15

    CPC classification number: G01C21/206 G01C21/165 G01C21/32 G01S5/0252 G01S19/13

    Abstract: A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building.

    Abstract translation: 描述了一个位置和地图服务,通过群众采集和数据融合技术创建了室内导航地图的全球数据库。 导航地图包括由跟踪的移动设备自动收集的多维传感器空间(例如,包括结构,RF,磁性,图像,声学或其他数据)中的地理参考的,独特描述的特征的数据库 移动通过建筑物(例如具有移动电话或机器人的人)。 当一个或多个跟踪设备穿过建筑物时,特征信息可用于创建建筑模型。

    Reducing elevation error with environmental pressure anomaly compensation
    24.
    发明授权
    Reducing elevation error with environmental pressure anomaly compensation 有权
    降低高程误差与环境压力异常补偿

    公开(公告)号:US09322648B1

    公开(公告)日:2016-04-26

    申请号:US14714209

    申请日:2015-05-15

    CPC classification number: G01C5/06

    Abstract: A method for determining an environmental pressure change affecting a pressure sensor within a portable device to determine an elevation of the portable device is disclosed. The method involves sampling pressure data from at least one stationary pressure sensor in an area surrounding a location of the device, wherein the stationary pressure sensor in not within the portable device. The sampled pressure data is then interpolated to a time interval and a difference is computed between the interpolated pressure data over each time interval to determine a differential pressure. The location of the stationary pressure sensor is determined and the differential pressure is added to a pressure map affecting data near the location. The environmental pressure change is then computed over any interval at the location and subtracted from a pressure measurement of the pressure sensor before computing an elevation of the portable device.

    Abstract translation: 公开了一种用于确定影响便携式设备内的压力传感器以确定便携式设备的高度的环境压力变化的方法。 该方法包括从围绕设备的位置的区域中的至少一个固定压力传感器采样压力数据,其中固定压力传感器不在便携式设备内。 然后将采样的压力数据内插到时间间隔,并且在每个时间间隔之间的内插压力数据之间计算差以确定差压。 确定固定压力传感器的位置,并将压差加到影响位置附近的数据的压力图上。 然后在该位置的任何间隔上计算环境压力变化,并在计算便携式设备的高程之前从压力传感器的压力测量中减去该压力变化。

    METHOD TO SCALE INERTIAL LOCATION DATA USING DIRECTIONAL AND/OR SCALE CONFIDENCE CONSTRAINTS
    25.
    发明申请
    METHOD TO SCALE INERTIAL LOCATION DATA USING DIRECTIONAL AND/OR SCALE CONFIDENCE CONSTRAINTS 审中-公开
    使用方向和/或规模的信任约束来定量实际位置数据的方法

    公开(公告)号:US20140278080A1

    公开(公告)日:2014-09-18

    申请号:US14212529

    申请日:2014-03-14

    CPC classification number: G01C21/165 G01C21/08

    Abstract: Methods, systems, and computer readable storage media are presented for directional scaling of inertial path data to satisfy ranging constraints. The presented techniques take into account scaling confidence information. In addition to bounding potential scale corrections based on the reliability of the inertial path and the magnetic heading confidence, the techniques bound potential scale parameters based on constraints and solve for directional scale parameters.

    Abstract translation: 呈现方法,系统和计算机可读存储介质用于方向缩放惯性路径数据以满足测距约束。 所提出的技术考虑到置信信息的扩展。 除了基于惯性路径的可靠性和磁标题置信度的边界潜在尺度校正之外,这些技术基于约束约束了潜在的尺度参数,并且解决了定向尺度参数。

    SYSTEMS AND METHODS FOR REDUNDANT INTEGRITY MONITORING

    公开(公告)号:US20230065658A1

    公开(公告)日:2023-03-02

    申请号:US17894660

    申请日:2022-08-24

    Abstract: System, computer program products, and methods for detecting and compensating for sensor interference. Sensor interference may result from environmental interference or from electronic signal interference. Sensor location input may be adapted or rejected when interference is detected. The system can monitor the accuracy, as well as the integrity, of all navigation sensors. The system can also automatically eliminate the faulty or compromised data from a final navigation solution.

    Crowd sourced mapping with robust structural features

    公开(公告)号:US10852145B2

    公开(公告)日:2020-12-01

    申请号:US15987774

    申请日:2018-05-23

    Abstract: A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building.

    Fusion of sensor and map data using constraint based optimization

    公开(公告)号:US10571270B2

    公开(公告)日:2020-02-25

    申请号:US15670881

    申请日:2017-08-07

    Abstract: Disclosed herein are methods and systems for fusion of sensor and map data using constraint based optimization. In an embodiment, a computer-implemented method may include obtaining tracking data for a tracked subject, the tracking data including data from a dead reckoning sensor; obtaining constraint data for the tracked subject; and using a convex optimization method based on the tracking data and the constraint data to obtain a navigation solution. The navigation solution may be a path and the method may further include propagating the constraint data by a motion model to produce error bounds that continue to constrain the path over time. The propagation of the constraint data may be limited by other sensor data and/or map structural data.

    Methods for improved heading estimation

    公开(公告)号:US10393542B2

    公开(公告)日:2019-08-27

    申请号:US15647004

    申请日:2017-07-11

    Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

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