使用EKF算法（拓展卡尔曼滤波）来估计机器人的位置信息，并实现可视化展示。该EKF算法还与里程计模型和GPS模型估计进行对比，来判断其估计效果。（运行时记得把.m文件改成英文，否则无法运行）

介绍两种目标跟踪算法—扩展卡尔曼滤波器（Extended Kalman Filter， EKF）、粒子滤波器（Particle filter， PF）。EKF利用泰勒级数方法，将非线性问题转化到线性空间，再利用卡尔曼滤波器进行滤波，并达到一阶估计精度。PF是一种采用蒙特卡罗采样的贝叶斯滤波方法，它将复杂的目标状态分布表示为一组加权值，通过寻找在粒子滤波分布中最大权值的粒子来确定目标最可能所处的状态分布，已成为复杂环境下进行目标跟踪的最好的方法。文中通过仿真实验，对二者的性能进行了仿真比较，结果证明在复杂的非高斯非线性环境中，PF的性能明显优于EKF，但计算复杂，耗时长。

In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors.We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of EKF-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the MSCKF algorithm) attains better accuracy, consistency, and computational efficiency than the SLAM formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF’s linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-IMU calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte-Carlo simulations and real-world testing. I

扩展卡尔曼滤波器（EKF） 3 非线性系统的卡尔曼滤波方程 3.1 扩展卡尔曼滤波器 非线性系统模型： 其中： 假设在 时刻已获得系统状态 的滤波估计 ，将 和 在 附近线性化，即非线性系统将随时在新估计的结果附近进行线性化。

扩展卡尔曼滤波（EKF）仿真演示 （西工大 严恭敏，2012-2-4）

基于C语言实现扩展卡尔曼滤波，文件包含具体实现代码

严恭敏老师编写的SINS的Matlab仿真程序，包括大方位误差角扩展卡尔曼（EKF），滤波初始对准仿真，静态惯导/星敏感器组合，大姿态误差角非线性状态模型，UKF滤波。

无人机开源飞控px4中 关于EKF2部分的详细推导！

PMSM 扩展卡尔曼滤波状态估计

matlab实现抛物线的EKF追踪，效果超好。具体的参考我的博客：https://blog.csdn.net/O_MMMM_O/article/details/106078679