Presentation given at the 2017 GTC about ZebraVision history.
This guide is not complete. It is merely the beginning on your journey with the TX1. It will not answer all of your questions nor is it meant to (It’s “incomplete” for a reason). Before you begin you should start by looking at the available resources you have and read the documentation available for the TX1. It’s entirely possible that this guide will be out of date by the time you read it. It is not definitive and should not be treated as such.
Presented by Marshall Massengill and Amrita Malur at the FIRST NC workshops.
How to get started with vision for FRC. This will include basic techniques, GRIP, using co-processors and OpenCV.
How to set up and use a Jetson TX1 with advanced object recognition techniques. Covers hardware setup such as powering the board and connecting to the robot network. Also covers vision coding techniques used to find retroreflective tape targets commonly found in FRC games.
Team900 was invited to nVidia's FRC First day to present this overview of their 2016 vision code
This project began as a thought exercise after one of our mentors had been playing with the new HERO board from Cross the Road Electronics (CTRE). The board was lightweight and versatile enough to be a very reliable robot control system.
This paper demonstrates team 900's efforts in using motion profiling on the Talon SRX. It contains a guide for motion profiling in LabVIEW with example code, but it is applicable to all languages. Questions about the paper can be posted here and someone who worked on this project will reply.
Document describing the structure, implementation and utilization of neural networks for tracking game objects on the field in real time. We used the Caffe library from Berkeley Vision, the OpenCV library, and the DIGITS software from NVIDIA to create the neural network. This specific network was developed to track boulders for the FRC 2015-2016 season but can be applied to track virtually any object with proper data collection and training.
Zebravision 4.0 is Team 900s vision system for the 2016 season; FIRST Stronghold. Our work was focused around recognizing the vision goals using shape and color based matching, recognizing the boulders using a neural network, and integrating the detection systems into a tracking system using the StereoLabs ZED stereo camera. This paper describes our methods of generating data for the neural networks. We took video of the ball using various lighting conditions and different, single-color backgrounds. We then identified the ball in these videos, took the clearest frames with the ball visible, and applied randomized shifts to the background in order to generate data.