Meet Dolly the robot

Ladies and gentlemen, please meet "Dolly the robot", the first version of my DIY mobile robot. My goal in this DIY project is to make a low-cost yet feature-rich ROS (Robot Operating System) based mobile robot that allow me to experiment my work on autonomous robot at home. To that end, Dolly is designed with all the basic features needed. To keep the bill of material as low as possible, i tried to recycle all of my spare hardware parts.


  • Robot's chassis is 3D printed, the chassis's plate design is borrowed from the design of Turtlebot 3 which is a smart design, IMO. The other hardware parts, however, are completely different from the Turtlebot 3.
  • IMU sensor with 9 DOF (accelerometer, magnetometer and gyroscope) for robot orientation measurement
  • Two DC motors with magnetic encoders using as wheels and odometer
  • Arduino Mega 2560 for low level control of the robot
  • Raspberry PI 3B+ with embedded Linux for high level algorithm and network communication. The ROS middle-ware on top of the Linux system offers a powerful robotic software environment
  • A 360 degree Neato LiDAR (laser scanner) up to 6 m range
  • A 8 Mega pixel camera (Raspberry PI camera)
  • Adafruit Motor shield V2 for motor controlling
  • 10000 Mah battery
  • ADS1115 analog sensor to measure and monitor battery usage
  • 0.95" (128x64) mini OLED display
  • The robot can be tele-operated using a bluetooth controller such as a PS4 controller


  • Localization and mapping (SLAM)
  • Obstacle avoidance
  • Autonomous navigation
  • Robot perception algorithms with LIDAR sensor and camera
  • Much more...
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)

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


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 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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