unscented_kalman_filter_cpp
所属分类：雷达系统
开发工具：C++
文件大小：925KB
下载次数：0
上传日期：2017-05-10 05:21:47
上 传 者：
sh-1993
说明： C语言中的无中心卡尔曼滤波器++
(Unscented Kalman Filter in C++)
文件列表:
CMakeLists.txt (704, 2017-05-10)
LICENSE (1066, 2017-05-10)
data (0, 2017-05-10)
data\obj_pose-laser-radar-synthetic-input.txt (66250, 2017-05-10)
init_tests.cc (124, 2017-05-10)
libs (0, 2017-05-10)
libs\Eigen (0, 2017-05-10)
libs\Eigen\Array (304, 2017-05-10)
libs\Eigen\CMakeLists.txt (607, 2017-05-10)
libs\Eigen\Cholesky (775, 2017-05-10)
libs\Eigen\CholmodSupport (1670, 2017-05-10)
libs\Eigen\Core (12826, 2017-05-10)
libs\Eigen\Dense (122, 2017-05-10)
libs\Eigen\Eigen (37, 2017-05-10)
libs\Eigen\Eigen2Support (3295, 2017-05-10)
libs\Eigen\Eigenvalues (1394, 2017-05-10)
libs\Eigen\Geometry (1605, 2017-05-10)
libs\Eigen\Householder (580, 2017-05-10)
libs\Eigen\IterativeLinearSolvers (1594, 2017-05-10)
libs\Eigen\Jacobi (645, 2017-05-10)
libs\Eigen\LU (983, 2017-05-10)
libs\Eigen\LeastSquares (712, 2017-05-10)
libs\Eigen\MetisSupport (697, 2017-05-10)
libs\Eigen\OrderingMethods (2189, 2017-05-10)
libs\Eigen\PaStiXSupport (1467, 2017-05-10)
libs\Eigen\PardisoSupport (864, 2017-05-10)
libs\Eigen\QR (926, 2017-05-10)
libs\Eigen\QtAlignedMalloc (637, 2017-05-10)
libs\Eigen\SPQRSupport (930, 2017-05-10)
libs\Eigen\SVD (858, 2017-05-10)
libs\Eigen\Sparse (594, 2017-05-10)
libs\Eigen\SparseCholesky (1433, 2017-05-10)
libs\Eigen\SparseCore (1835, 2017-05-10)
libs\Eigen\SparseLU (1776, 2017-05-10)
libs\Eigen\SparseQR (991, 2017-05-10)
libs\Eigen\StdDeque (749, 2017-05-10)
libs\Eigen\StdList (682, 2017-05-10)
... ...
# Unscented Kalman Filter in C++
An Unscented Kalman Filter that uses a Constant Turn Rate and Velocity (CTRV) motion model in C++. This Ukf fuses LIDAR and RADAR sensor readings.
**WIP**
---
## Dependencies
* cmake >= v3.5
* make >= v4.1
* gcc/g++ >= v5.4
## Basic Build Instructions
1. Clone this repo.
2. Make a build directory: `mkdir build && cd build`
3. Compile: `cmake .. && make`
4. Run it: `./UnscentedKF path/to/input.txt path/to/output.txt`. You can find
some sample inputs in 'data/'.
- eg. `./UnscentedKF ../data/obj_pose-laser-radar-synthetic-input.txt output.txt`
## Output you should See
```
mez:build/ (master) $ ./TestUKF [22:11:07]
===============================================================================
No tests ran
mez:build/ (master) $ ./UnscentedKF ../data/obj_pose-laser-radar-synthetic-input.txt output.txt [22:11:10]
Accuracy - RMSE:
0.0597576
0.0865825
0.331234
0.215768
Done!
```
*Even though linear approximation using a Jacobian matrix stinks ;)*
---
1. Check out my implementation of an [Extended Kalman Filter in C++](https://github.com/mez/extended_kalman_filter_cpp)
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