TODOLIST:

1. make the visualization

Introduction

Assume the robot has been kidnapped and transported to a new location! Luckily it has a map of this location, a (noisy) GPS estimate of its initial location, and lots of (noisy) sensor and control data.

In this project we will implement a 2 dimensional particle filter in C++. Our particle filter will be given a map and some initial localization information (analogous to what a GPS would provide). At each time step your filter will also get observation and control data.

Running the Code

Once you have this repository on your machine, cd into the repository's root directory and run the following commands from the command line:

> ./clean.sh
> ./build.sh
> ./run.sh

If everything worked you should see something like the following output:

.
.
.
Time step: 100
Cumulative mean weighted error: x 39.8926 y 9.60949 yaw 0.198841
Your x error, 39.8926 is larger than the maximum allowable error, 1

Implementing the Particle Filter

The directory structure of this repository is as follows:

root
|   build.sh
|   clean.sh
|   CMakeLists.txt
|   README.md
|   run.sh
|
|___data
|   |   control_data.txt
|   |   gt_data.txt
|   |   map_data.txt
|   |
|   |___observation
|       |   observations_000001.txt
|       |   ... 
|       |   observations_002444.txt
|   
|___src
    |   helper_functions.h
    |   main.cpp
    |   map.h
    |   particle_filter.cpp
    |   particle_filter.h

Inputs to the Particle Filter

You can find the inputs to the particle filter in the data directory.

The Map*

map_data.txt includes the position of landmarks (in meters) on an arbitrary Cartesian coordinate system. Each row has three columns

1. x position
2. y position
3. landmark id

Control Data

control_data.txt contains rows of control data. Each row corresponds to the control data for the corresponding time step. The two columns represent

1. vehicle speed (in meters per second)
2. vehicle yaw rate (in radians per second)

Observation Data

The observation directory includes around 2000 files. Each file is numbered according to the timestep in which that observation takes place.

These files contain observation data for all "observable" landmarks. Here observable means the landmark is sufficiently close to the vehicle. Each row in these files corresponds to a single landmark. The two columns represent:

1. x distance to the landmark in meters (right is positive) RELATIVE TO THE VEHICLE.
2. y distance to the landmark in meters (forward is positive) RELATIVE TO THE VEHICLE.