Asian Surveying & Mapping
Breaking News
High-resolution spatial maps to assess climate-related shocks
Insurance companies and governments worldwide are increasingly using spatial...
Aurecon strengthens digital offering in Greater China to help clients future-proof their infrastructure
Hong Kong – As businesses across Asia continue to...
Synspective and GCRS Announce Partnership for SAR Satellite-Based Risk Analysis Solutions in South Asia
Geo Climate Risk  Solutions Pvt. Ltd. (GCRS), a solution...
Teledyne Optech Galaxy T2000 mobilized for earthquake recovery and reconstruction effort in China
Vaughan, Ontario, CANADA – Teledyne Geospatial announced that the State...
Presagis Teams with Kambill Systems to Provide Artificial Intelligence-Based Geospatial Services in Asia Pacific
First two Contracts Awarded by Indian National Survey Agency/State...
Synspective and GCRS Announce Partnership for SAR Satellite-Based Risk Analysis Solutions in South Asia
2022 November 15, Tokyo – Geo Climate Risk Solutions...
Fugro opens state-of-the-art space control centre SpAARC in Perth
Fugro has officially opened the Australian Space Automation, Artificial...
Chinese scientists create new detailed map of moon rocks
BEIJING - Chinese scientists have created a high-resolution map...
Russia and Iran expand space cooperation
Russia and Iran are gradually expanding their cooperation in...
Korea bolsters spatial data cooperation with Tanzania, Ethiopia
The government will help Tanzania and Ethiopia with effective...
  • Jul 30, 2019
  • Comments Off on Researchers from University of Adelaide Win Global Pose Estimation Challenge
  • Feature
  • 681 Views

The team of researchers from the University of Adelaide’s Australian Institute for Machine Learning (AILM) defeated 47 other universities and space technology companies at the international space competition hosted by the European Space Agency.

The South Australian team—including Associate Professor Tat-Jun Chin, Dr. Bo Chen and Dr. Alvaro Parra Bustos—won the challenge to determine the most accurate orientation of an object in space by using machine learning and 3D vision algorithms.

Teams were given individual high-fidelity images of the Tango spacecraft from the 2016 PRISMA mission and were required to determine the orientation of the craft in relation to the observer from close rendezvous.

The goal of the challenge was to estimate the pose—the relative position and attitude—of a known spacecraft in order to help future space missions.

Knowing the exact pose enables the development of debris-removal technologies, refurbishment of expensive space assets, and the development of space depots to facilitate travel toward distant destinations.