Hypermaps: closing the complexity gap in robotic mapping
Description of the granted funding
Hypermaps improves how robots manage data about the environment they inhabit. The most common way for robots to handle environmental information is by using maps. At present, each different kind of data is hosted on a separate map. Similarly to how our use of maps have evolved from single-purpose maps (geographical, political, road map, etc.) to multi-layer maps (like Google Maps) which present us task-relevant information automatically, we propose a multi-layer mapping framework for robots. Hypermaps simplifies the way robots access maps and helps them correlate information from different maps. This enables robots to increase their ability to understand the world around them, perform more advanced tasks than what they can today, better understand user requests, and autonomously correct their knowledge about the environment. The research will be conducted in Aalto University, in collaboration with University of Bonn, Technical University of Munich, KONE ltd, and GIM Robotics.
Show moreStarting year
2023
End year
2027
Granted funding
Funder
Research Council of Finland
Funding instrument
Academy research fellows
Other information
Funding decision number
354909
Fields of science
Electronic, automation and communications engineering, electronics
Research fields
Automaatio- ja systeemitekniikka
Identified topics
robots, robotics