Feature-based EKF SLAM on Turtlebot3 from Scratch

Overview

This repository builds feature-based EKF SLAM on Turtlebot3 from scratch. The demo below shows the algorithm in action (2x speed).

• Trajectories
  • The pink path shows the odometer estimated path.
  • The green path is the groundtruth.
  • Yello path is SLAM results.
• Landmarks
  • Blue landmarks are groundtruth.
  • Green landmarks are measurements.
  • Indigo landmarks show where the SLAM algorithm thinks their positions are.

The figure below shows the result of the landmark detection algorithm using a 2D laser scanner:

The system has the following major components:

• A 2D Lie Group library for differential drive robots with complete unit testing
• A waypoint following feedback controller
• Turtlebot3 URDF built from scratch for Gazebo simulation
• Gazebo plugins to control the robot and return the groundtruth data for evaluation
• An odometer that estimates robot states based on encoder reading
• Turtlebot3 interface that controls the motors with given velocity command
• Feature detection algorithm that identifies landmarks using a 2D laser scanner
• EKF SLAM algorithm that estimates robots states

A detailed description can be found in my portfolio.

File structure

Packages

Six packages are in this repository.

• nuturtle_description develops Turtlebot3 URDF, and visualizes the wheeled robot in Rviz
• nuturtle_gazebo includes Gazebo plugins to simulate the robot in Gazebo
• nuturtle_robot implements the Turtlebot3 interface, and includes the test node for the odometer on the real robot
• nuturtle_slam includes the feature detection algorithm and the EKF SLAM algorithm
• tsim implements the waypoints following feedback controller
• rigid2d is the 2D Lie Group library, including SO(2), SE(2) calculations, the odometer, and the fake encoder

Major launch files

• nuturtle_description/launch/view_diff_drive.launch launches the URDF model in Rviz
• nuturtle_robot/launch/test_waypoint.launch drives the robot through waypoints using the fake encoder with visualization in Rviz
  • Service /start_waypoint would start the movement
• nuturtle_gazebo/launch/diff_drive_gazebo.launch drives the robot through waypoints in Gazebo simulation
• nuturtle_slam/launch/landmarks.launch launches the landmark detection algorithm with visualization in Rviz
• nuturtle_slam/launch/slam_in_control.launch launches the actual SLAM algorithm
  • Rviz visualization is in map frame
  • Pink path shows the odometer estimated path.
  • Green path is the groundtruth.
  • Yello path is SLAM results.
  • Blue landmarks are groundtruth.
  • Green landmarks are measurements.
  • Indigo landmarks show where the SLAM algorithm thinks their positions are.

Dependencies

• ROS Melodic (Link)
• Eigen (Official Site)

Quick Start guide

• Install all the dependencies
• fork this repository, then clone the package using wstool
  • rosinstall file is included in the repository
• Build the package using catkin_make
• Use roslaunch nuturtle_slam slam_in_control to launch the SLAM algorithm

Future work

• Data association is currently assumed to be known. SLAM with unknown data association can be achieved by calculating the Mahalanobis Distance