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

• URL: http://arxiv.org/abs/2408.01035v1
• Date: Fri, 2 Aug 2024 06:18:39 GMT
• Title: Structure from Motion-based Motion Estimation and 3D Reconstruction of Unknown Shaped Space Debris
• Authors: Kentaro Uno, Takehiro Matsuoka, Akiyoshi Uchida, Kazuya Yoshida,
• Abstract summary: This paper proposes the Structure from Motion-based algorithm to perform unknown shaped space debris motion estimation with limited resources. The method is validated with the realistic image dataset generated by the microgravity experiment in a 2D air-floating testbed and 3D kinematic simulation.
• Score: 3.037387520023979
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Event-based Structure-from-Orbit [23.97673114572094]
  Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera. We propose event-based structure-from-orbit (eSf), where the aim is to reconstruct the 3D structure of a fast spinning object observed from a static event camera.
  arXiv  Detail & Related papers  (2024-05-10T03:02:03Z)
• Instantaneous Perception of Moving Objects in 3D [86.38144604783207]
  The perception of 3D motion of surrounding traffic participants is crucial for driving safety. We propose to leverage local occupancy completion of object point clouds to densify the shape cue, and mitigate the impact of swimming artifacts. Extensive experiments demonstrate superior performance compared to standard 3D motion estimation approaches.
  arXiv  Detail & Related papers  (2024-05-05T01:07:24Z)
• Reconstructing Satellites in 3D from Amateur Telescope Images [44.20773507571372]
  This paper proposes a framework for the 3D reconstruction of satellites in low-Earth orbit, utilizing videos captured by small amateur telescopes. The video data obtained from these telescopes differ significantly from data for standard 3D reconstruction tasks, characterized by intense motion blur, atmospheric turbulence, pervasive background light pollution, extended focal length and constrained observational perspectives. We validate our approach using both synthetic datasets and actual observations of China's Space Station, showcasing its significant advantages over existing methods in reconstructing 3D space objects from ground-based observations.
  arXiv  Detail & Related papers  (2024-04-29T03:13:09Z)
• SpatialTracker: Tracking Any 2D Pixels in 3D Space [71.58016288648447]
  We propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth estimators. Tracking in 3D allows us to leverage as-rigid-as-possible (ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts.
  arXiv  Detail & Related papers  (2024-04-05T17:59:25Z)
• Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting [0.0]
  We present an approach for mapping of satellites on orbit based on 3D Gaussian Splatting. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms.
  arXiv  Detail & Related papers  (2024-01-05T00:49:56Z)
• Uncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecasting [79.34357055254239]
  Hand trajectory forecasting is crucial for enabling a prompt understanding of human intentions when interacting with AR/VR systems. Existing methods handle this problem in a 2D image space which is inadequate for 3D real-world applications. We set up an egocentric 3D hand trajectory forecasting task that aims to predict hand trajectories in a 3D space from early observed RGB videos in a first-person view.
  arXiv  Detail & Related papers  (2023-07-17T04:55:02Z)
• GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction [61.833152949826946]
  We propose a novel Ground-aware Motion Model for 3D Human Motion Reconstruction, named GraMMaR. GraMMaR learns the distribution of transitions in both pose and interaction between every joint and ground plane at each time step of a motion sequence. It is trained to explicitly promote consistency between the motion and distance change towards the ground.
  arXiv  Detail & Related papers  (2023-06-29T07:22:20Z)
• Machine Learning in Orbit Estimation: a Survey [1.9336815376402723]
  It is estimated that around one million objects larger than one cm are currently orbiting the Earth. Current approximate physics-based methods have errors in the order of kilometers for seven-day predictions. We provide an overview of the work in applying Machine Learning for Orbit Determination, Orbit Prediction, and atmospheric density modeling.
  arXiv  Detail & Related papers  (2022-07-19T00:17:27Z)
• Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning [1.212848031108815]
  We propose an end-to-end active visual tracking method based on DQN algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to arbitrary space non-cooperative target merely relied on color or RGBD images. It significantly outperforms position-based visual servoing baseline algorithm that adopts state-of-the-art 2D monocular tracker, SiamRPN.
  arXiv  Detail & Related papers  (2021-12-18T06:12:24Z)
• Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation [76.58256020932312]
  Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task. We present a self-supervised learning framework for 3D object motion field estimation from monocular videos.
  arXiv  Detail & Related papers  (2021-10-13T16:45:01Z)
• Gravity-Aware Monocular 3D Human-Object Reconstruction [73.25185274561139]
  This paper proposes a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos. We focus on scenes with objects partially observed during a free flight. In the experiments, our approach achieves state-of-the-art accuracy in 3D human motion capture on various metrics.
  arXiv  Detail & Related papers  (2021-08-19T17:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.