sharathsrini / extended-kalman-filter-for-sensor-fusion

Extended Kalman Filters Are useful when there is an Non Linearity in the su=ystem and the estimation/prediction and measurement step requires a Jacobian matrix( first Derivative in the Taylor Series) is required to transform and work optimally.

Extended Kalman Filter Project

In this project I have utilized an Extended Kalman Filter algorithm to estimate the state of a moving object of interest with noisy lidar and radar measurements. Passing the project requires obtaining RMSE values that are lower that the tolerance outlined in the project rubric.

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Important Dependencies

• cmake >= 3.5
  • All OSes: click here for installation instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW

Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
   • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
4. Run it: ./ExtendedKF

Results

Visualisation

The following graph compares real and estimated values for car coordinates using data from Dataset 1

RMSE

The accuracy requirement is that the algortihm should perform with RMSE error lower than some threshold values. This shown in tables below for both datasets:

Dataset 1:

Parameter	RMSE	RMSE threshold
x	0.0974	0.11
y	0.0855	0.11
Vx	0.4517	0.52
Vy	0.4404	0.52

Dataset 2:

Parameter	RMSE	RMSE threshold
x	0.0726	-
y	0.0967	-
Vx	0.4579	-
Vy	0.4966	-