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How to develop state-of-the-art LIDAR perception algorithms with MATLAB

Overview

As the field of automated driving evolves, the need for highly reliable perception systems becomes increasingly critical, particularly at higher levels of automation. LIDAR (Light Detection and Ranging) technology has emerged as a vital component of modern automated driving systems, owing to its ability to provide accurate, high-resolution, and real-time detection and measurement of surrounding objects.

In this presentation, we will discuss how to build LIDAR based development workflows for perception and navigation in autonomous driving application, covering the critical stages of data acquisition, processing, and interpretation.

This presentation will give you insights on:

  • Accessing and streaming LIDAR data
  • Simulating LIDAR sensor in synthetic environments
  • LIDAR camera cross calibration
  • Apply deep learning algorithms on LIDAR point cloud
  • Map building and SLAM using LIDAR data
  • Object tracking on sequential point cloud data

About the Presenters

Mr. Minhaj Palakkaparambil Mohammed is a product manager at MathWorks, with a focus on autonomous systems and LIDAR point cloud processing. Prior to joining Math Works, he worked as a lead engineer for developing autonomous systems. He holds a master’s degree from NIT Suratkal in India.

Dimitri Hamidi is a senior application engineer at MathWorks since 2018. He received a diploma degree in electrical and information technology engineering from the Technical University of Munich. Prior to joining MathWorks, he served as a research associate at the German Aerospace Center (DLR) and as an algorithm engineer at Continental.

Product Focus

How to develop state-of-the-art LiDAR perception algorithms with MATLAB

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