Self-Supervised Learning on Mobile Robots Using Acoustics, Vibration, and Visual Models to Build Rich Semantic Terrain Maps

3305 Newell-Simon Hall

Abstract: Humans and robots would benefit from having rich semantic maps of the terrain in which they operate.  Mobile robots equipped with sensors and perception software could build such maps as they navigate through a new environment.  This information could then be used by humans or robots for better localization and path planning, as well [...]