Hypermaps aim at improving 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 kind of data is hosted on a separate map, which complicates planning because a robot attempting to perform a tasks needs to access and process information from many different maps.
The use of maps for humans have moved away from single-purpose maps (such as geographical, political, and road map) to favor multi-layer maps (like Google Maps or OpenStreetMap) which store different information on each layer. We propose that in robotics a similar shift has the potential to revolutionize the way robots interact with knowledge about their environment.
One of the immediate benefits of the proposed framework is simplifying the interaction between the robot and the underlying maps, treated in the framework as layers. The framework works as an interface for the robot, that will receive information about the map content only by communicating with the framework. However many new possibilities open when all maps are managed together, and the long term plan of this project is to demonstrate this potential.
- Francesco Verdoja (email@example.com), postdoctoral researcher
- Ville Kyrki (firstname.lastname@example.org), professor
Video Presentation at PAL workshop @ ICRA2020 “On the Potential of Smarter Multi-layer Maps in Robotics”
Watch the video presentation of our latest paper in the Hypermaps project
We presented the core idea behind the Hypermaps project at the ICRA 2020 workshop on Perception, Action, Learning (PAL)
The source code for the article “Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration” is available on github.
Pre-print of “Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration” available on arXiv
Our paper has been uploaded on arXiv