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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.

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sharathsrini/Extended-Kalman-Filter-for-Sensor-Fusion

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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.


Important Dependencies

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

alt text

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 -

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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.

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