Particle Filter Localization Project

Project for the Self-Driving Car Nanodegree Program

Your robot has been 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 have implement a 2 dimensional particle filter in C++. The particle filter is given a map and some initial localization information (analogous to what a GPS would provide). At each time step the filter also get observation and control data.

Tasks

The only file you should modify is particle_filter.cpp in the src directory. The file contains the scaffolding of a ParticleFilter class and some associated methods.

Inputs to the Particle Filter

You can find the input 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:

x position
y position
landmark id

Map data provided by 3D Mapping Solutions GmbH.

Simulator input

All other data the simulator provides, such as observations and controls. The values provided by the simulator to the program are:

• Noisy position data from the simulator

    ["sense_x"]
    ["sense_y"]
    ["sense_theta"]
  

• Previous velocity and yaw rate to predict the particle's transitioned state

    ["previous_velocity"]
    ["previous_yawrate"]
  

• Receive noisy observation data from the simulator, in a respective list of x/y values

   ["sense_observations_x"]
   ["sense_observations_y"]
  

Particle Filter Outputs

• best particle values used for calculating the error evaluation

    ["best_particle_x"]
    ["best_particle_y"]
    ["best_particle_theta"]
  

Optional message data used for debugging particle's sensing and associations

• Respective (x,y) sensed positions ID label

    ["best_particle_associations"]
  

• Respective (x,y) sensed positions

    ["best_particle_sense_x"] <= list of sensed x positions
    ["best_particle_sense_y"] <= list of sensed y positions
  

Contributing

Google's C++ style guide

Build

Linux

There are two scripts provided to build and clean the server:

• Clean binaries
• Build binary

Docker

To build, compile and run the code we use a docker image together with CLion.

• To build the image run

    docker build -t dev/env .
  

• To run the image

    docker run -p 127.0.0.1:2222:22 -p 127.0.0.1:4567:4567 --name particle-filter-env --rm dev/env 
  

The code can be copy using ssh, then use cmake to setup and make to build. Finally run the particle_filter executable.

For more details of the Clion integration go to the post Using Docker with CLion

Execute

• Execute the run.sh script
• Run the Term 2 Simulator and select the Project 3.

References

• Project Definition

License

MIT License Copyright (c) 2016-2018 Udacity, Inc.