恶劣光照下基于事件-RGB融合的航天器位姿估计
Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting
AI总结：
针对航天器位姿估计中单一传感器在恶劣光照下的局限性，提出基于分束棱镜对齐与RANSAC融合的事件-RGB位姿估计方法，结合dropout不确定性检测极端条件，并构建了公开的真实数据集验证其有效性。
摘要：
航天器位姿估计对于自主空间操作（如交会、对接和在轨服务）至关重要。基于视觉的位姿估计方法通常采用RGB成像传感器，是航天器位姿估计的极具吸引力的解决方案，但会受到恶劣光照条件的挑战，这些条件会产生眩光、过曝、泛光和镜头眩光等成像伪影。由于具有更高的动态范围，神经形态或事件传感器对极端光照条件具有更强的适应能力。然而，事件传感器通常空间分辨率较低，并且在低相对运动期间信噪比会降低。这项工作通过引入一种结合RGB和事件传感器的传感器融合方法来解决这些单个传感器的局限性。采用分束棱镜实现精确的光学和时间对齐。随后，开发了一种基于RANSAC的技术来融合来自RGB和事件通道的信息，以实现利用两种模态优势的位姿估计。该流程还结合了dropout不确定性估计，以检测影响任一通道的极端条件。为了对所提出的事件-RGB融合方法的性能进行基准测试，我们在实验室环境中收集了在各种具有挑战性的照明条件下用于卫星位姿估计的全面RGB和事件真实数据集。在该数据集上的令人鼓舞的结果证明了我们的事件-RGB融合方法的有效性，并进一步支持了事件传感器在航天器位姿估计中的使用。为了支持社区在该主题上的研究，我们的数据集已公开发布。
英文摘要：
Spacecraft pose estimation is crucial for autonomous in-space operations, such as rendezvous, docking and on-orbit servicing. Vision-based pose estimation methods, which typically employ RGB imaging sensors, is a compelling solution for spacecraft pose estimation, but are challenged by harsh lighting conditions, which produce imaging artifacts such as glare, over-exposure, blooming and lens flare. Due to their much higher dynamic range, neuromorphic or event sensors are more resilient to extreme lighting conditions. However, event sensors generally have lower spatial resolution and suffer from reduced signal-to-noise ratio during periods of low relative motion. This work addresses these individual sensor limitations by introducing a sensor fusion approach combining RGB and event sensors. A beam-splitter prism was employed to achieve precise optical and temporal alignment. Then, a RANSAC-based technique was developed to fuse the information from the RGB and event channels to achieve pose estimation that leveraged the strengths of the two modalities. The pipeline was complemented by dropout uncertainty estimation to detect extreme conditions that affect either channel. To benchmark the performance of the proposed event-RGB fusion method, we collected a comprehensive real dataset of RGB and event data for satellite pose estimation in a laboratory setting under a variety of challenging illumination conditions. Encouraging results on the dataset demonstrate the efficacy of our event-RGB fusion approach and further supports the usage of event sensors for spacecraft pose estimation. To support community research on this topic, our dataset has been released publicly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
