# 使用 Transformer 辅助卡尔曼滤波器的未知航天器相对单目导航

> 原文链接：https://robotics.hellobike.com/papers/2026-10-05-2610-07231
> 频道：papers | 更新日期：2026-10-05 | 标签：arxiv, cs.RO
> 核心摘要：提出结合Transformer网络与MSCKF的单目位姿估计管线，并在SPE3R合成航天器渲染轨迹上验证未知目标导航。

# 使用 Transformer 辅助卡尔曼滤波器的未知航天器相对单目导航

::: v-pre
> 原文链接：[Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter](https://arxiv.org/abs/2610.07231)（arXiv 2610.07231 · 发布日期：2026-10-05）

**Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter**

提出结合Transformer网络与MSCKF的单目位姿估计管线，并在SPE3R合成航天器渲染轨迹上验证未知目标导航。

## 核心要点

- 针对现有视觉方法依赖目标先验、额外传感或仅恢复尺度不定平移的问题。
- Transformer基于SuperPoint与LightGlue匹配特征估计尺度不定图像间里程计。
- MSCKF融合伪测量与轨道、姿态运动学模型，估计相对位姿相关状态。
- 在留出航天器渲染轨迹仿真中，中位姿态误差3.7°，ROE距离误差2.2%。

**验证范围与局限**：仅在重渲染高分辨率SPE3R合成图像及留出航天器渲染轨迹的蒙特卡洛仿真中验证。

## 英文摘要 (Abstract)

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.

作者：Pol Francesch Huc, Simone D’Amico
:::

## 原始参考来源 (Sources)

- [arXiv 2610.07231：Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter](https://arxiv.org/abs/2610.07231)