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.
Abstract (translated)
空间中未知物体的精确状态估计是一项关键挑战，其应用范围从跟踪太空碎片到估算小型天体的形状。实现这一能力的一个必要条件是能够在连续流图像中找到并追踪特征。现有的方法如SIFT、ORB和AKAZE能够实现实时但不够准确的姿态估计，而现代深度学习方法虽然能提供更高质量的特征，却需要更多的计算资源，这可能在航天器专用硬件上不可用。此外，无论是经典方法还是数据驱动的方法，在面对目标物体上由自身投射出的高度不透明阴影时均不具备鲁棒性。我们展示了随着目标天体旋转，这些阴影可能导致结果姿态估计中的大幅偏差。对于这类天体而言，实时姿态估计算法中的偏置可能会误导航天器的状态估算器，并导致任务失败，特别是在该物体经历混沌翻滚运动的情况下。 我们提出了COFFEE（Celestial Occlusion Fast FEature Extractor），这是一种专门为小行星设计的实时姿态估计框架，旨在利用太阳追踪传感器提供的日相角先验信息。通过将显著轮廓与其投影阴影关联起来，可以检测到一组稀疏但不受阴影移动影响的特征点。接着，一种稀疏神经网络与基于注意力机制的图神经网络配准模型共同训练，以提供连续帧之间的对应关系集合。 实验证明，该姿态估计流程是无偏的、比经典方法更准确，并且在合成数据和翻滚小行星阿波菲斯的渲染图像上，相较于其他最先进的深度学习流程快了一个数量级。
URL
https://arxiv.org/abs/2508.03132
