ROS Live Class: SLAM Mapping and Localization for Robots

Added:

ROSject Setup
Navigation Basics
SLAM Prep
Configure GMapping
Create a Map
Save the Map
Setup Localization
Localize Robot
Q&A Discussion

ROSject Setup

6:09
Playing Section
  • 1

    Shows how to access and share a ROSject for the live class.

  • 2

    Explains how to open a shared project in the ROS Development Studio.

  • 3

    Guides users to the Jupyter notebook with class instructions.

Basic understanding of the ROS (Robot Operating System) ecosystem, including nodes, topics, messages, and launch files.
Familiarity with the ROS TF (Transform) library and how coordinate frames (such as map, odom, and base_link) relate to each other.
Fundamental concepts of robot kinematics, odometry data, and 2D laser scanners (LiDAR).
Conceptual understanding of probabilistic robotics and the difference between localization and mapping.
Integrating localization (AMCL) and maps with the ROS Navigation Stack (move_base) for autonomous path planning and obstacle avoidance.
Exploring advanced 3D SLAM packages (such as RTAB-Map or Google Cartographer) using RGB-D cameras or 3D LiDAR.
Deep-diving into tuning AMCL covariance parameters, costmaps, and recovery behaviors for robust deployment in dynamic environments.
Studying multi-robot SLAM and collaborative localization techniques.
18.5K views282likes1:16:01@TheConstructOriginal Release: 2019-03-12

This video teaches how to create maps of robot environments using SLAM techniques with GMapping and how to localize robots within those maps using AMCL algorithms, which are essential foundations for autonomous robot navigation in ROS.