Skip to main content
European Commission logo print header

Robotics advancement through Web-publishing of sensorial and elaborated extensive data sets

Objective

The aim of the RAWSEEDS project is to stimulate and support progress in autonomous robotics by providing a comprehensive, high-quality benchmarking toolkit. The absence of standard benchmarks is a widely acknowledged problem in the field, and is doubly harmful to it: firstly, because it impedes recognition of scientific and technical progress, thus discouraging research and development; and secondly, because it prevents new actors (and particularly SMEs) from entering the robotic sector, as heavy investments are needed to compensate for that absence. RAWSEEDS will also perform all the actions needed for a rapid and thorough dissemination of its results through the academic and industrial domains (e.g. setup of a website, documentation and support actions, workshops, competitions, publications). The benchmarking toolkit that RAWSEEDS will create includes: high-quality multi-sensorial data sets, benchmark problems based on them, state-of-the-art solutions to these problems in the form of algorithms and software, and methodologies for the assessment of algorithms.

The RAWSEEDS project will obtain its objectives through the following actions:
1. Definition of a set of high-quality benchmarks and methodologies for the assessment of algorithms and software for autonomous robotics applications. The benchmarks will be focused on the problems of sensorial data analysis, sensor fusion, localization, mapping and Simultaneous Localization And Mapping (SLAM).
2. Creation of a website from which researchers and enterprises will be able to download these benchmarks, contribute new material and communicate with each other.
3. Dissemination of knowledge about the RAWSEEDS benchmarks and website.

Call for proposal

Data not available

Coordinator

POLITECNICO DI MILANO
EU contribution
No data
Address
PIAZZA LEONARDO DA VINCI 32
MILANO
Italy

See on map

Links
Total cost
No data

Participants (3)