Build and Deploy Simultaneous Localization and Mapping (SLAM) Workflows with MATLAB

Overview

Simultaneous Localization and Mapping (SLAM) enables autonomous systems, such as self-driving cars and smart devices like virtual reality headsets, to navigate unknown environments. SLAM achieves this by building a virtual map of the physical world and localizing the platform within that map at the same time. It utilizes onboard sensors, including cameras, LiDARs, and IMUs, to estimate the platform's pose (position and orientation) and trajectory.

Join our webinar for an in-depth exploration of SLAM techniques, where we will delve into its core principles including front-end and back-end algorithms, applications in automotive and robotics, and the typical challenges faced during implementation. Participants will discover a range of SLAM algorithms available in MATLAB® and Simulink®, with demonstrations of advanced techniques for sensor fusion, pose and factor graph optimization, and deployment with ROS.

Highlights

Key Topics Covered:

• Monocular Visual SLAM: Learn how to implement high-performance, deployable monocular visual SLAM in MATLAB using real-world data.
• 3D LiDAR SLAM: Explore 3D LiDAR SLAM techniques with pose graph optimization.
• Multi-Sensor SLAM Workflows: Dive into workflows using factor graphs, with a focus on monocular visual-inertial systems (VINS-Mono).
• SLAM Deployment: Understand how to deploy SLAM algorithms with seamless MATLAB and ROS integration.

This webinar is designed for professionals and enthusiasts looking to deploy SLAM solutions as a part of their autonomous system workflow.

About the Presenters

Minhaj Falaki is a product manager at MathWorks, with a focus on perception and mapping for autonomous systems. He has been actively involved with the automotive and aerospace industries in advancing lidar point cloud processing workflows. Prior to joining MathWorks, Minhaj worked as a lead engineer for developing autonomous systems. Minhaj holds a master’s degree in mechatronics engineering from NIT Suratkal in India.

Jose Avendano is a Senior Robotics engineer from MathWorks specialized in robotics research and education. Other previous experience includes modeling, simulation, and algorithm development for robot manipulators, autonomous rendezvous and collaboration for zero-gravity navigation, and robust sensing and control of flexible vehicle structures.