利用神经辐射场估计接近操作中未知空间目标的位姿
Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations
AI总结：
本文提出利用可学习外观嵌入的神经辐射场从稀疏图像生成多样视角与光照数据，使原本依赖已知CAD模型的现成航天器位姿估计网络能够用于未知目标，并在SPEED+硬件在环图像上验证了其与基于CAD合成数据训练相当的估计性能。
摘要：
我们解决的是未知目标航天器相对于单目相机的6D位姿估计问题，这是未来主动碎片清除任务所需的自主交会和接近操作的关键一步。我们提出了一种新方法，使一个“现成的”航天器位姿估计器——该估计器原本需要已知目标CAD模型——能够应用于未知目标。我们的方法依赖于一种自然场景中的NeRF，即一种神经辐射场，它采用可学习的外观嵌入来表示自然场景中遇到的变化光照条件。我们使用描绘目标的稀疏图像集合训练NeRF模型，进而生成一个在视角和光照方面都具有多样性的庞大数据集。该数据集随后用于训练位姿估计网络。我们在SPEED+的硬件在环图像上验证了我们的方法，这些图像模拟了接近在轨遇到的光照条件。我们证明，我们的方法成功实现了从稀疏图像集合训练现成的航天器位姿估计网络。此外，我们还表明，使用我们的方法训练的网络性能与使用目标CAD模型生成的合成图像训练的模型相当。
英文摘要：
We address the estimation of the 6D pose of an unknown target spacecraft relative to a monocular camera, a key step towards the autonomous rendezvous and proximity operations required by future Active Debris Removal missions. We present a novel method that enables an "off-the-shelf" spacecraft pose estimator, which is supposed to known the target CAD model, to be applied on an unknown target. Our method relies on an in-the wild NeRF, i.e., a Neural Radiance Field that employs learnable appearance embeddings to represent varying illumination conditions found in natural scenes. We train the NeRF model using a sparse collection of images that depict the target, and in turn generate a large dataset that is diverse both in terms of viewpoint and illumination. This dataset is then used to train the pose estimation network. We validate our method on the Hardware-In-the-Loop images of SPEED+ that emulate lighting conditions close to those encountered on orbit. We demonstrate that our method successfully enables the training of an off-the-shelf spacecraft pose estimation network from a sparse set of images. Furthermore, we show that a network trained using our method performs similarly to a model trained on synthetic images generated using the CAD model of the target.
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