The Project

In this project a kalman filter is used to estimate the state of a moving object of interest with noisy lidar and radar measurements.

Prerequisites

This project involves the Term 2 Simulator which can be downloaded here

Required tools are:

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

This repository includes two files that can be used to set up and install uWebSocketIO for either Linux or Mac systems. For windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO. Please see this concept in the classroom for the required version and installation scripts.

Build and Install

Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.

mkdir build
cd build
cmake ..
make
./ExtendedKF

Interaction with the Term 2 Simulator

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program

[“sensor_measurement”] => the measurement that the simulator observed (either lidar or radar)

OUTPUT: values provided by the c++ program to the simulator

[“estimate_x”] <= kalman filter estimated position x
[“estimate_y”] <= kalman filter estimated position y
[“rmse_x”]
[“rmse_y”]
[“rmse_vx”]
[“rmse_vy”]

Code Style

I tried to stick to the Google’s C++ style guide. In order to check the guidelines I installed cpplint using pip install cpplint

Results

The simulator provides noisy lidar and radar measurements which are shown as blue and red dots. The position that is calculated by the kalman filter is displayed as green dots.

The results for dataset 1 are shown in the following image alt text

The results for dataset 2 are shown in the following image alt text

CarND-Extended-Kalman-Filter-Project