基于Transformer辅助卡尔曼滤波的未知航天器单目导航
Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter
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中文总结 AI 辅助
提出一种结合Transformer网络与MSCKF的单目视觉管线，仅用单目相机即可估计未知航天器位姿，在SPE3R数据集上验证，姿态误差3.7°，距离误差2.2%。
中文摘要 AI 辅助
本工作提出了一种新颖的基于学习的管线，用于仅利用来自单个服务航天器的单目图像对未知航天器进行位姿估计。该方法将基于Transformer的神经网络与多状态约束卡尔曼滤波器（MSCKF）相结合，以在整个交会和近距离操作过程中估计位姿（即目标航天器相对于相机的位置和姿态）。与现有的基于视觉的方法不同，这些方法需要预先知道目标形状或惯性特性，依赖深度、激光雷达或立体视觉等额外传感模态，或仅恢复带尺度的平移，所提出的管线使用单个单目相机可推广到以前未见过的航天器。Transformer网络从由LightGlue匹配的SuperPoint特征估计里程计，即图像之间带尺度的位姿变化。MSCKF利用这些伪测量值以及轨道和姿态运动学模型来估计目标的位姿。特别是，相对轨道要素、目标相对于服务航天器相机的姿态以及相关的角速度由滤波器直接估计。鉴于单目方法和与目标的短距离，通过服务航天器的姿态机动恢复了目标距离的完全可观测性。该方法在SPE3R数据集的高分辨率重渲染版本上进行训练和评估，该数据集包含103个航天器的合成图像。其中十一个航天器在训练期间被保留，以评估对未见目标的泛化能力。然后使用蒙特卡洛模拟在保留航天器的渲染轨迹上评估导航管线。结果表明，作为卡尔曼滤波器前端的学习视觉管线在绕未知目标导航时，姿态中值误差为3.7°，相对轨道要素（ROE）中距离误差为2.2%。
英文摘要
This work presents a novel learning-based pipeline for pose estimation of unknown spacecraft using only monocular images from a single servicer. The approach combines a transformer-based neural network with a Multi-State Constraint Kalman Filter (MSCKF) to estimate the pose (i.e., position and orientation of the target spacecraft relative to the camera) throughout rendezvous and proximity operations. Unlike existing vision-based methods that require prior knowledge of the target shape or inertia properties, rely on additional sensing modalities such as depth, lidar, or stereo, or only recover translation up to scale, the proposed pipeline generalizes to previously unseen spacecraft using a single monocular camera. The transformer network estimates the odometry, the change in pose between images up to scale, from SuperPoint features matched by LightGlue. The MSCKF uses these pseudo-measurements along with an orbit and attitude kinematics model to estimate the pose of the target. In particular, the relative orbit elements, the target's attitude with respect to the servicer's camera, and the associated angular velocity are estimated directly by the filter. Given the monocular approach and short distance to the target, the full observability of the range to the target is recovered via attitude maneuvers by the servicer. The method is trained and evaluated on a re-rendered high-resolution version of the SPE3R dataset, which includes synthetic images of 103 spacecraft. Eleven of these spacecraft are held out during training to evaluate the generalization to unseen targets. Monte Carlo simulations are then used to evaluate the navigation pipeline on rendered trajectories of the held out spacecraft. The results demonstrate that learned vision pipelines as a front-end for Kalman filters provide median errors of 3.7° in attitude and 2.2% of range in ROE when navigating about unknown targets.
发表机构
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
