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The space around the Earth is home to abandoned satellites, rocket boosters, and other bits of space debris that have accumulated over the years. It is estimated that there are tens of thousands of space debris in low Earth orbit (LEO). At least 34,000 orbiting pieces of junk are larger than 10 cm each, which make them a major threat because they are orbiting the Earth at very high speeds. There were two near collisions recently, which could have been disastrous: In September, the ISS was forced to conduct a maneuver to avert a possible collision with an unknown piece of space debris, while there were high chances of a Chinese ChangZheng-4c rocket crashing into an old Soviet Parus navigation satellite in October.

With numerous missions being conducted by several space organizations, space must be made a safer place for all future explorations. Toward that objective, technologies are being developed to capture and deorbit space debris. One such pioneering mission is ClearSpace, a spin-off stemmed from the EPFL Space Center (eSpace). The first mission of the project, ClearSpace-1 scheduled for 2025, is to recover the now obsolete Vespa Upper Part, a payload adapter orbiting 660 kilometers above the Earth that was once part of the European Space Agency’s Vega rocket.

At least three EPFL laboratories are working in tandem on the project: Computer Vision Laboratory led by Professor Pascal Fua, Realistic Graphics Lab led by Assistant Professor Wenzel Jakob, and Embedded Systems Lab spearheaded by Professor David Atienza (also a faculty member at EcoCloud).

ClearSpace-1 aims to extricate the debris from space by enabling the robotic arms of a captured rocket to approach the Vespa from the correct angle, grasp the object, and pull it back into the atmosphere. That calls for the development of deep learning algorithms to estimate the 6D pose (3 rotations and 3 translations) of the target based on video-sequences. That’s where the Computer Vision Laboratory comes in with inputs from Mathieu Salzmann, the lead scientist working on the project.

However, the scientists must allow for physical changes in the Vespa over the seven years of its space wanderings. To estimate its current appearance for Professor Salzmann, the Realistic Graphics Lab is generating a database of synthetic images of the target object, which includes a detailed 3D model of the Vespa upper stage.

The nature of the project requires the scientists to ensure completion of the mission in space with limited computing power onboard. The Embedded Systems Lab is leading that segment of the work of transferring the deep learning algorithms to a dedicated hardware platform. As Professor Atienza explains, “Since motion in space is well behaved, the pose estimation algorithms can fill the gaps between recognitions spaced one second apart, alleviating the computational pressure.” However, he adds, “…the algorithms are so complex that their implementation requires squeezing out all the performance from the platform resources.”

Therefore, the key to the success of the mission is to design algorithms that are 100% reliable in space. And therein lies the challenge, as well as the inherent attraction, of the entire project for the scientists at EPFL.