Autonomous driving is the current hot topic in automotive industry. The utilization of Simultaneous Localization and Mapping (SLAM) is commonly found in autonomous navigation. This project presents the idea of examining SLAM algorithms by implementing such an algorithm on a custom bot which has been fitted with sensors and microcontrollers. The software architecture is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications.
This Project covers, the examining of these algorithms in both simulations, and real-world experiments. The method used in this project is more related to v-development cycle, meaning that a near model of the vehicle is first implemented in simulations using each algorithm and followed by real world experiment.
This project has brought about a dynamic model of a small-scale bot which can be utilized for recreation of any ROS-compliant SLAM-algorithm, and this model has been used to compare diffrent SLAM algorithm with diffrent scenarios.
This Repository contains launch files for Gmapping, Hector Map and Rtab Map. Please navigate through the readme files to know more about simulation and real-time implementation in respective folders
Click here for our article on ROS.
Hardware and software used for this project:
Software
ROS: Kinetic Kame
OS: Ubuntu 16.04
SIM: Gazebo 7.0.0
Hardware
MASTER PC
RAM: 8GB DDR4
Storage: Samsung EVO SSD
CPU: Intel i5 (8th Gen)
GPU: Nvidia MX150 4GB
REMOTE PC
Raspberry pi3 B+
OTHERS
RP LIDAR A1M8
Microsoft Kinect Sensor (first gen)
SPG30E-200K DC Geared Motor with Encoder 17 RPM, 80 N.cm, 12V
L298N 2A Based Motor Driver Module