SDCND Term 2 Project 6: Extended Kalman Filter

Project for Udacity Self-Driving Car Engineer Nanodegree Program

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

This project includes the implementation of an Extended Kalman filter with C++. A Udacity-provided simulator (available for download here generates noisy RADAR and LIDAR measurements of an object's position and speed, and the Extended Kalman Filter [EKF] must merge these measurements to predict the object's position. Communication between the simulator and the EKF takes place via uWebSocket.

Udacity's project basis can be found here.

Dependencies

cmake >= 3.5
make >= 4.1
gcc/g++ >= 5.4
Udacity's simulator

For instructions on how to install these components on different operating systems, visit Udacity's project.

Setup and Running

Follow these suggested steps for Windows setup:

Follow these the instructions for setting up Ubuntu BASH.
Download Windows simulator here.
Open Ubuntu Bash (write all following commands to Ubuntu Bash command window)
sudo apt-get update
sudo apt-get install git
sudo apt-get install cmake
sudo apt-get install gcc
sudo apt-get install g++
sudo apt-get install openssl
sudo apt-get install libssl-dev
navigate to where you want to clone this repository to, for example: cd /mnt/c/Users/"Bob"
git clone https://github.com/autonomobil/SDCND-P6_Extended-Kalman-Filter
sudo rm /usr/lib/libuWS.so
navigate to project folder: cd ./SCDND-P6_ExtendedKalmanFilter
./install-ubuntu.sh
mkdir build && cd build
cmake .. && make
Launch the term2_sim.exe from Windows simulator folder
Execute ./ExtendedKF
If you see Listening to port 4567 Connected!!!, it is working
Press Start

These files were modified compared to the original repository:

src/FusionEKF.cpp
src/kalman_filter.cpp
src/tools.cpp

Checking Rubric points

Your code should compile.

The code compiles without errors on my setup following the instructions above.

px, py, vx, vy output coordinates must have an RMSE <= [.11, .11, 0.52, 0.52] when using the file: "obj_pose-laser-radar-synthetic-input.txt" which is the same data file the simulator uses for Dataset 1.

Dataset 1 RMSE : [0.0964, 0.0853, 0.4154, 0.4316]
Dataset 2 RMSE : [0.0727, 0.0966, 0.4400, 0.4755]

Your Sensor Fusion algorithm follows the general processing flow as taught in the preceding lessons

Sensor Fusion takes place at ./src/FusionEKF.cpp
./src/FusionEKF.cpp uses Predict, Update and UpdateEKF from ./src/kalman_filter.cpp kalman_filter.cpp

Your Kalman Filter algorithm handles the first measurements appropriately

First measurement is handled in lines 54 - 101 ./src/FusionEKF.cpp

Your Kalman Filter algorithm first predicts then updates

Prediction: Lines 103 - 131 ./src/FusionEKF.cpp; using ./src/kalman_filter.cpp
Update: Lines 132 - 150 ./src/FusionEKF.cpp; using ./src/kalman_filter.cpp

Your Kalman Filter can handle radar and lidar measurements

In ./src/FusionEKF.cpp if-statements decide how to initialize and process the data given the sensor type

autonomobil/SDCND-P6_Extended-Kalman-Filter