Implement Simultaneous Localization and Mapping (SLAM) Algorithms with MATLAB

Simultaneous Localization and Mapping or SLAM algorithms are used to develop a map of an environment and localize the pose of a platform or autonomous vehicle in that map. SLAM algorithms allow the platform to map out unknown environments. Engineers use the map information to carry out tasks such as path planning and obstacle avoidance.

SLAM algorithms are useful in many applications such as navigating a fleet of mobile robots to arrange shelves in a warehouse, parking a self-driving car in an empty spot, or delivering a package by navigating a drone in an unknown environment. MATLAB^® and Simulink^® provide SLAM algorithms, functions, and analysis tools to develop various mapping applications. You can implement simultaneous localization and mapping along with other tasks such as sensor fusion, object tracking path planning, and path following.

In this video, you will learn about implementing 2D SLAM algorithm using Navigation Toolbox™. Two new products were introduced in R2019b to complement the capabilities of Robotics System Toolbox™: Navigation Toolbox and ROS Toolbox.