Project-Where-am-I

Use the Adaptive Monte Carlo Localization Algorithm in ROS to estimate our Robot!

Description

This repo has reference to the project Where Am I of the Robotics Software Engineer Nanodegree program offered by Udacity. This project focuses on the localisation of a robot using AMCL algorithm, in a gazebo-simulated mapped environment. Create a ROS package that launches a custom robot model in a custom Gazebo world. Utilize the ROS AMCL package and the Tele-Operation / Navigation Stack to localize the robot. Explore, add, and tune specific parameters corresponding to each package to achieve the best possible localization results.

Installation of some ROS packages

You could install them as shown below:

$ sudo apt-get install ros-kinetic-navigation
$ sudo apt-get install ros-kinetic-map-server
$ sudo apt-get install ros-kinetic-move-base
$ sudo apt-get install ros-kinetic-amcl

Perhaps update your system

$ sudo apt-get update && sudo apt-get upgrade -y

Building the Project

1. Clone this repository into src folder of the workspace.

$ cd /home/workspace/catkin_ws/src
$cd ..

2. Build the project using the following commands:

$ cd /home/workspace/catkin_ws
$ catkin_make
$ source devel/setup.bash

3. Execute the project using the following commands in separate terminal of same directory(cd /home/workspace/catkin_ws):

$ roslaunch my_robot world.launch 
$ roslaunch myamcl amcl.launch

Rviz Configuration

- Select: Global Options-> Fixed Frame-> Map

- Add Robot Model.

- Add Camera. Then Select Image Topic: /camera/rgb/image_raw.

- Add LaserScan. Then Select Image Topic: /scan.

- Add Maps. Then Select Image Topic: /map. Check other maps such as /move_base/local_costmap/costmap /move_base/global_costmap/costmap

- Add PoseArrow. Then Select Topic: /pointcloud.

Testing

You have two options to control your robot while it localize itself here:

• Send navigation goal via RViz
• Send move command via teleop package. Run this command:

$ teleop_twist_keyboard teleop_twist_keyboard.py 

Navigate your robot, observe its performance and tune your parameters for AMCL!

Screenshot of the Project