Crowdsourcing as a methodology to obtain large and varied robotic data sets

Abstract

For autonomous robots to operate successfully in unknown environments, their computer vision algorithms need to generalize over many different environments. However, due to practical considerations robotic vision experiments are typically limited to a single robot and a few (laboratory) environments. We propose crowdsourcing as a methodology for gathering large and varied robotic data sets. We evaluate the methodology by performing the first crowdsourcing experiment involving actual robots. In particular, we have made a space-game called `Astro Drone' for a toy quad rotor, the Parrot AR drone. Nine months after the game's release, there are 14,628 downloads and 840 contributions, consisting of visual features and drone state estimates. Data mining shows the methodology's potential, providing insights such as the relation between the number of visual features and obstacle distances.