Sensor fusion(传感器融合)

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Sensor fusion(传感器融合)

From Wikipedia, the free encyclopedia 来自维基百科,免费的百科Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individua…

扩展卡尔曼滤波EKF与多传感器融合

参考:https://blog.csdn.net/young_gy/article/details/78468153 Extended Kalman Filter(扩展卡尔曼滤波)是卡尔曼滤波的非线性版本.在状态转移方程确定的情况下,EKF已经成为了非线性系统状态估计的事实标准.本文将简要介绍EKF,并介绍其在无人驾驶多传感器融合上的应用. KF与EKF 本文假定读者已熟悉KF,若不熟悉请参考卡尔曼滤波简介. KF与EKF的区别如下: 预测未来:x′=Fx+u用x′=f(x,u)代替:其余F用F…

Udacity carnd2 Sensor Fusion, Extended Karman Filter (English)

Extended Karman Filter Zhenglei 2018 January This is a project to estimate the car position from Lidar data and Radar data. Lidar data is using Kalman filter. Radar data is using Extended Karman filter, because the radar data is provided by non-linea…

车载多传感器融合定位方案：GPS +IMU+MM

导读高德定位业务包括云上定位和端上定位两大模块.其中,云上定位主要解决Wifi指纹库.AGPS定位.轨迹挖掘和聚类等问题:端上定位解决手机端和车机端的实时定位问题.近年来,随着定位业务的发展,用户对在城市峡谷(高楼.高架等)的定位精度提出了更高的要求. 特别是车机端定位,由于定位设备安装在车上,一方面,它可以搭载更丰富的定位传感器来解决特殊场景的问题,另一方面,各个传感器之间相互固连,有利于高精度的算法设计.这两点为车机端进一步提高导航精度的提供了可能. 城市峡谷一直是车机端定位的痛点.原因是…

Sensor Fusion-based Exploration in Home Environments using Information, Driving and Localization Gains(基于传感器融合的使用信息、驾驶和定位增益在家庭环境中的探索)

Authors: Joong-Tae Park, Jae-Bok Song Department:Department of Mechanical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, South Korea(机械工程系,高丽大学,韩国) Exploration is one of the most important functions for a mobile service ro…