COFFEE: A Shadow-Resilient Real-Time Pose Estimator for Unknown Tumbling Asteroids using Sparse Neural Networks

• URL: http://arxiv.org/abs/2508.03132v1
• Date: Tue, 05 Aug 2025 06:27:14 GMT
• Title: COFFEE: A Shadow-Resilient Real-Time Pose Estimator for Unknown Tumbling Asteroids using Sparse Neural Networks
• Authors: Arion Zimmermann, Soon-Jo Chung, Fred Hadaegh,
• Abstract summary: We present COFFEE, a real-time pose estimation framework for asteroids.<n>By associating salient contours to their projected shadows, a sparse set of features are detected.<n>The resulting pose estimation pipeline is found to be bias-free, more accurate than classical pose estimation pipelines.
• Score: 7.976380956275378
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The accurate state estimation of unknown bodies in space is a critical challenge with applications ranging from the tracking of space debris to the shape estimation of small bodies. A necessary enabler to this capability is to find and track features on a continuous stream of images. Existing methods, such as SIFT, ORB and AKAZE, achieve real-time but inaccurate pose estimates, whereas modern deep learning methods yield higher quality features at the cost of more demanding computational resources which might not be available on space-qualified hardware. Additionally, both classical and data-driven methods are not robust to the highly opaque self-cast shadows on the object of interest. We show that, as the target body rotates, these shadows may lead to large biases in the resulting pose estimates. For these objects, a bias in the real-time pose estimation algorithm may mislead the spacecraft's state estimator and cause a mission failure, especially if the body undergoes a chaotic tumbling motion. We present COFFEE, the Celestial Occlusion Fast FEature Extractor, a real-time pose estimation framework for asteroids designed to leverage prior information on the sun phase angle given by sun-tracking sensors commonly available onboard spacecraft. By associating salient contours to their projected shadows, a sparse set of features are detected, invariant to the motion of the shadows. A Sparse Neural Network followed by an attention-based Graph Neural Network feature matching model are then jointly trained to provide a set of correspondences between successive frames. The resulting pose estimation pipeline is found to be bias-free, more accurate than classical pose estimation pipelines and an order of magnitude faster than other state-of-the-art deep learning pipelines on synthetic data as well as on renderings of the tumbling asteroid Apophis.

Related papers

• Seurat: From Moving Points to Depth [66.65189052568209]
  We propose a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories.<n>Our approach achieves temporally smooth, high-accuracy depth predictions across diverse domains.
  arXiv  Detail & Related papers  (2025-04-20T17:37:02Z)
• Vision-Based Detection of Uncooperative Targets and Components on Small Satellites [6.999319023465766]
  Space debris and inactive satellites pose a threat to the safety and integrity of operational spacecraft. Recent advancements in computer vision models can be used to improve upon existing methods for tracking such uncooperative targets. This paper introduces an autonomous detection model designed to identify and monitor these objects using learning and computer vision.
  arXiv  Detail & Related papers  (2024-08-22T02:48:13Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects [1.0205541448656992]
  We present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO) The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object.
  arXiv  Detail & Related papers  (2023-03-17T13:53:59Z)
• SU-Net: Pose estimation network for non-cooperative spacecraft on-orbit [8.671030148920009]
  Spacecraft pose estimation plays a vital role in many on-orbit space missions, such as rendezvous and docking, debris removal, and on-orbit maintenance. We analyze the radar image characteristics of spacecraft on-orbit, then propose a new deep learning neural Network structure named Dense Residual U-shaped Network (DR-U-Net) to extract image features. We further introduce a novel neural network based on DR-U-Net, namely Spacecraft U-shaped Network (SU-Net) to achieve end-to-end pose estimation for non-cooperative spacecraft.
  arXiv  Detail & Related papers  (2023-02-21T11:14:01Z)
• Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking [98.91894395941766]
  We propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame. Specifically, we derive this prediction of dynamics through a graph neural network(GNN) that explicitly accounts for both spatial-temporal and visual information. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
  arXiv  Detail & Related papers  (2021-06-07T16:36:50Z)
• Object-based Illumination Estimation with Rendering-aware Neural Networks [56.01734918693844]
  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene.
  arXiv  Detail & Related papers  (2020-08-06T08:23:19Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)
• Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks [0.0]
  In this paper, a convolutional neural network is leveraged to determine the translation and rotation of an object of interest relative to the camera. The simulation framework designed for assembly task is used to generate dataset for training the modified CNN models. It is shown that the model performs comparable to the current feature-selection methods and can therefore be used in conjunction with them to provide more reliable estimates.
  arXiv  Detail & Related papers  (2020-01-29T02:53:52Z)

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.