Sensor fusion with Extended Kalman filter

Overview

This project utilizes an extended Kalman filter to estimate the state of a moving object of interest with noisy LIDAR and RADAR measurements.

Dependencies

• Simulator
• uWebSockets
• cmake
• gcc/g++
• make
• openssl
• libuv
• zlib

Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd $_
3. Compile: cmake .. && make

Run Instructions

• To run with sample data ./ExtendedKF input_file <output_file>
• To run with simulator ./ExtendedKF

Plotting data

Input and output data can be plotted using plot.py.

Usage: python plot.py input_data.txt output_data.txt

Accuracy

The following image shows the EKF estimates:

The following shows change in RMSE over time:

The EKF accuracy was:

• Dataset 1 : RMSE <= [0.0973, 0.0855, 0.4513, 0.4399]
• Dataset 2 : RMSE <= [0.0726, 0.0965, 0.4216, 0.4932]