Controlling a Turtlebot using PhaROS: Goals planning and Automatic docking

This is a demonstration of my current work on controlling robot using ROS and PhaROS. For that task, I've developed a dedicated PhaROS package that defines:

  1. A base framework for ROS based visualization such as map, robot model, robot trajectory, etc.
  2. An Event-driven API for robot controlling
28/08/2018 ROS, SLAM, Metrics, evaluation, SSIM, MSE, NE

Evaluation of grid maps produced by a SLAM algorithm

When evaluating the performance of a SLAM algorithm, quantifying the produced map quality is one of the most important criteria. Often, the produced map is compared with (1) a ground-truth map (which can be easily obtained in simulation) or (2) with another existing map that is considered accurate (in case of real world experiment where the ground-truth is not always available ).

Basically, grid maps are images, so image similarity measurement metrics can be used in this case. In this post, we consider three different metrics: Mean Square Error (MSE), K-nearest based normalized error (NE) and Structure Similarity Index (SSIM)

Adding noise to odometry data published by the Gazebo simulator

I've had funny time playing around with the Gazebo simulator for autonomous robot exploration. One thing I've encountered is that the odometry data provided by Gazebo is so perfect that, sometime, makes the simulation less realistic. I used a Turtlebot model as robot model in my simulations. Googling around, i didn't find any solution of adding noise to the odometry data of this robot (using the URDF file). I then decided to develop a dedicated ROS node allowing me to add some random noise to the Gazebo's odometry data.

The robot motion model

First thing fist, we need to understand the robot motion model. There are many motion models, but in the scope of this article, we focus only on the odometry motion model. Often, odometry is obtained by integrating sensor reading from wheel encoders, it measures the relative motion of the robot between time \(t\) and \(t-1\) or \((t-1,t]\). In 2D environment, a robot pose is represented by a point \((x,y)\) and an orientation (rotation angle) \(\theta\), so the robot pose at the time \(t-1\) and \(t\) are denoted by:
$$p_{t-1}=(x_{t-1},y_{t-1},\theta_{t-1})$$
$$p_{t}=(x_t,y_t,\theta_t)$$

Setting up a software stack for autonomous mono robot exploration and mapping using ROS

Prerequisites

This setup is performed and documented on an Ubuntu system with the following software stack:

  • Ubuntu 16.04 LTS (may work on other distribution though)
  • ROS kinetic: if you don't have ROS pre-installed, please refer to this tutorial http://wiki.ros.org/kinetic/Installation/Ubuntu. I suggest to use the full desktop installation configuration, this will take a while (> 2 GB)
  • Gazebo for simulation (it is installed by default if you choose the full desktop installation when installing ROS)
  • GIT (sudo apt-get install git)
  • Catkin for package building (installed by default when installing ROS)

To follow this post, some basic knowledge on ROS is needed:

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