M2M Day 198: Whoa, it’s even better than I hoped!
This post is part of Month to Master, a 12-month accelerated learning project. For May, my goal is to build the software part of a self-driving car.
Yesterday, I seriously struggled: I was trying to convert Udacity’s ROSbag files into JPEGs and CSVs, so I could use the data for training my self-driving car, but I didn’t have much luck.
Ultimately, I discovered that the Robot Operating System is not compatible with Mac, and so, I couldn’t properly extract the files locally on my computer.
Today, after a lot of trial and error, I was able to figure out how to run Ubuntu 14.04 and ROS on a virtual machine using VirtualBox.
After even more trial and error, I figured out how to use the virtual machine to extract the contents of the ROSbag files…
I was expecting to find a reasonably-sized one-camera set of images, and a CSV for corresponding steering angle.
Instead, the Udacity dataset includes ~33,000 frames of driving video from three different camera angles and all the data for steering, braking, throttle, GPS, etc.
Within the steering CSV, for example, the data includes, not only timestamp and angle, but also torque (rotational force on the wheel) and speed (turning speed).
Anyway, this dataset is super cool, and much more thorough than I expected. I’m excited to see how I can use this data to make a more functional, end-to-end self-driving car.
Read the next post. Read the previous post.
