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SYSTEMS AND METHODS FOR DEEP LEARNING-BASED PEDESTRIAN DEAD RECKONING FOR EXTEROCEPTIVE SENSOR-ENABLED DEVICES

机译：基于深度学习的行人死亡的系统和方法，用于避开Extlepive传感器的设备

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摘要

Systems are provided for estimating 6DOF positioning of a computing device while in a pedestrian dead reckoning mode. The systems obtain a set of inertial tracking data from the set of one or more inertial tracking components while the system is in a pedestrian dead reckoning mode. Then, the systems obtain an estimated 3DOF velocity of the system based inertial tracking data, using a predictive model trained on a set of observed exteroceptive sensor data and observed inertial tracking data. The systems also obtain estimated 6DOF positioning of the systems based on the estimated 3DOF velocity.

机译：提供系统，用于估计计算设备的6dof定位，同时在行人死亡的回忆模式下。系统从一组或多个惯性跟踪组件中获取一组惯性跟踪数据，而系统处于行人死亡的再推算模式。然后，使用基于系统的惯性跟踪数据的系统获得估计的3DOF，使用在一组观察到的extoelplive传感器数据和观察到的惯性跟踪数据上培训的预测模型。该系统还基于估计的3DOF速度获得系统的估计6DOF定位。

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公开/公告号US2021262800A1

专利类型
公开/公告日2021-08-26

原文格式PDF
申请/专利权人 MICROSOFT TECHNOLOGY LICENSING LLC;
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申请/专利号US202016797798
发明设计人 EVAN GREGORY LEVINE;RAYMOND KIRK PRICE;
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申请日2020-02-21
分类号G01C21/16;G01S5/02;G01S5/14;G06F3/0346;G06N7;H04W4/02;G02B27/01;G06T19;
国家 US
入库时间 2022-08-24 20:48:14

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