Unscented Kalman Filter

Background.

In the previous lesson we saw the Extended Kalman filters and how it can be applied to a linear transformation. As you know original kalman filter had linear transformations and gaussian distribution as hard assumptions. It benefits from gaussian distributions remaining gaussian when a linear transformation is applied to them. It also benefits from the product of two gaussian distributions also being a gaussian. Extended kalman filter approximates a non-linear transformation by replacing it with a linear transformation that has the same derivatives at the mean. The resulting linear transformation will transform the original gaussian into a new gaussian, even though the real non-linear transformation would have reshaped that distribution. Unscented Kalman Filter uses the gaussian assumption to convert the mean and covariance matrix into a collection of representative points on that gaussian, then applies the non-linear transformation to get a new set of points

Build instructions

Assuming you have 'cmake' and 'make' already:

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./UnscentedKF along with the Term 2 simulator.

Rubric Points

Compilation

Code compiles fine without any errors or warnings, flag is set for -Wall

kputtur@osboxes:~/Udacity/CarND-Unscented-Kalman-Filter-Project/build$ make
[ 25%] Building CXX object CMakeFiles/UnscentedKF.dir/src/ukf.cpp.o
[ 50%] Building CXX object CMakeFiles/UnscentedKF.dir/src/main.cpp.o
[ 75%] Building CXX object CMakeFiles/UnscentedKF.dir/src/tools.cpp.o
[100%] Linking CXX executable UnscentedKF
[100%] Built target UnscentedKF

Accuracy

Plot overlay of ground truth, sensor measurements and estimate position

Accuracy is within limits for the data set.

RMSE Dataset 1:

X  = 0.0715
Y  = 0.0832
VX = 0.2050
VY = 0.2473

Even though dataset 2 is not a requirement.

RMSE Dataset 2:

X = 0.1095
Y = 0. 0710
VX = 0.7083
VY = 0.4054

Position accuracy was excellent.

Velocity estimated accuracy suffers significant at the beginning of the run.

Radar and Lidar measurements

NIS measurements for both LIDAR and RADAR:

Tuning Parameters

This project can be tuned using various intialization parameters to improve the final calculated RMSE(Root mean squared error). In the ukf.cpp file, I have tuned std_a, std_yawd, the initialized x_ (separately for Radar and Lidar) and P_ (also separate for each sensor type).

Attribution

The bulk of the code is from my class room exercises from the udacity class on self driving cars, lesson 7 "Unscented Kalman Filters". The infrastructure code in this project is supplied from project repository. Visualization is from here : https://github.com/udacity/CarND-Mercedes-SF-Utilities