Structure from Motion-based Motion Estimation and 3D Reconstruction of Unknown Shaped Space Debris

TL;DR

The paper tackles the challenge of estimating the motion and reconstructing the shape of unknown-shaped space debris using Structure from Motion from sequences of 2D images. It proposes a complete, resource-efficient pipeline comprising camera calibration, background removal, SfM reconstruction, point-cloud denoising, and a principled motion-parameter calculation that yields translation and rotation relative to the debris. Validation on a 2D air-floating microgravity testbed and a 3D kinematic simulation demonstrates accurate 2D velocity and rotation estimates (2D: ~5% linear, ~2.3% angular; 3D: RMSE < 1 mm/s and <0.005 deg/s) and robust shape reconstruction. The approach enables reliable autonomous debris capture and manipulation with limited onboard computation by providing a noncontact method to predict debris motion and geometry.

Abstract

With the boost in the number of spacecraft launches in the current decades, the space debris problem is daily becoming significantly crucial. For sustainable space utilization, the continuous removal of space debris is the most severe problem for humanity. To maximize the reliability of the debris capture mission in orbit, accurate motion estimation of the target is essential. Space debris has lost its attitude and orbit control capabilities, and its shape is unknown due to the break. This paper proposes the Structure from Motion-based algorithm to perform unknown shaped space debris motion estimation with limited resources, where only 2D images are required as input. The method then outputs the reconstructed shape of the unknown object and the relative pose trajectory between the target and the camera simultaneously, which are exploited to estimate the target's motion. The method is quantitatively validated with the realistic image dataset generated by the microgravity experiment in a 2D air-floating testbed and 3D kinematic simulation.

Figures (10)

• Figure 1: Concept of the Structure from Motion (SfM)-based motion estimation. SfM is usually applied to static objects with a moving camera; however, for the motion estimation of space objects, the stationary camera observes a free-flying target in orbit. Even so, SfM output is the same as the original use case, and the relative pose trajectory is exploited to estimate the target's motion parameters.
• Figure 2: Pictures of the microsatellite mock-up exposed to the parallel intense light beam in a dark room with the different camera's aperture value: $f$. When $f$ is too small, (a) the reflective surface of the satellite becomes overexposed, resulting in unsuccessful SfM computation. However, (b) an appropriate f-number eliminates this issue, enabling proper feature detection.
• Figure 3: Possible in-orbit image examples (left) and the results of the background treatment where the target spacecraft was precisely extracted (right) (Images credit: © NASA).
• Figure 4: Principle for motion estimation from Structure from Motion output. Using the target frame $\Sigma_T$ defined by the user and the time sequential camera frame $\Sigma_{C_t}$ obtained by SfM, a sequence of the radius vectors (solid arrows) from the target to the moving camera are cumulatively computed. Target linear and angular velocity are estimated by measuring the change of the radius vector's norm and rotation, respectively.
• Figure 5: Experimental setup with air-floating testbed to emulate two-dimensional microgravity motions. Motion capture system was used to get the ground truth for comparison.