Glüxkind leverages AI to build the world's smartest baby stroller that solves new parents' mobility challenges and accelerates pedestrian autonomy. We are combining AI, Computer vision and robotics to create a pedestrian robotics platform.

In order to be able to apply artificial intelligence to computer vision data, large data sets are required to train the machine learning and path planning algorithms. The project is intended to help grow the data base of vision data.

The students will also have the chance to get unique insights into this innovative technology and various areas of software development ranging from computer vision, artificial intelligence, machine learning, robotics control, path planning to classing web & app development.

Data Processing

The main part of the project will be learning the basics of Computer Vision, Machine Learning that enable autonomous driving. In order to develop robust machine learning models a large data base needs to be developed. To do that images need to be labelled and classified with relevant categories that help the robot identify traversable and non-traversable areas and in turn move safely in real world environments.

The project offers great flexibility in terms of when the students choose to complete the allocated data processing tasks.

Data Collection

IF students are locally in Vancouver, they can assist with the data collection in urban areas of the City.