Perception Localization and Mapping for Mobile Robot
Completed - 2022 | C++ - ROS 1 - ROS2 - Nav2 - Odometry - SLAM
University Project
Final group assignments of a course in Perception Localization and Mapping for Mobile Robots.
Implementation of Mobile Robot navigation simulation with ROS1, using Real robot data collected in ROS bags.
- Implemented Odometry estimation for an autonomous shuttle, with ROS and C++.
- Mapped an indoor environment from ROS bag Laser Scan and 3D Lidar data, using slam_toolbox.
- Denoised the map, and performed autonomous navigation from csv waypoints, using AMCL (Adaptive Monte Carlo Localization) for localization and Navigation stack for path generation and tracking.
First poject
Main objectives:
- Derived kinematic model of an autonomous shuttle.
- Used encoder wheel information to reconstruct the autonomous shuttle odometry.
Second project
Main objectives:
- Mapped the environment explored by a mobile robot, using data collected in a ROS bag.
- Used both 2D Lidar and 3D Lidar (converted in 2D information).
- Relied on a ros_stage simulation environment for Navigation.
- Tuned navigation parameters to optimize performance.
- Performed autonomous waypoint-follow on that map, using points from a .csv file.
Read more details on the repo README.
ROS2 SLAM Course (Udemy)
Course on Nav2 by Edouard Renard.
Exploring ROS2 open source libraries for Mapping and Navigation tasks, with a turtlebot robot in gazebo.
Further details on each lesson on my GitHub README Notes.