Presentation: The Basics of ROS Applied to Self-Driving Cars

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 10:40am - 10:50am

Day of week: Tuesday

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Abstract

The Robot Operating System, or ROS, is a framework used for robotics around the world. With the emergence of self-driving cars, many companies have helped moved ROS from an academic tool to a core component in the development of their autonomous vehicles. This talk will cover a brief overview of the ROS architecture and how you would begin using it on your self-driving car project.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Anthony Navarro

Product Lead- Robotics and Self-Driving Car @Udacity

Anthony currently works as a Product Lead for Udacity leading their Self-Driving Car and Robotics Nanodegree programs in Mountain View, California. After combat service in the US Army, he obtained his Master’s in Computer Engineering from Colorado State University where he focused his studies on the area of robotics. Prior to his employment at Udacity, Anthony worked as a systems and software engineer for Lockheed Martin for 7 years where he supported multiple satellite launches and operations and also conducted applied research and development on autonomous systems. While at Lockheed Martin, he was selected to participate in the Advanced Technical Leadership Program offered by the company. Prior to joining Udacity, he also led the Udacity/ PolySync self-driving car race and was a student in the first cohort of the Udacity Self-Driving Car Nanodegree program.

Find Anthony Navarro at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.