Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representations, or low-fidelity outputs unsuitable for robotic operations. This work proposes a novel approach to incrementally reconstruct and track a dynamic unknown object using a unified representation -- a set of 3D Gaussian blobs that describe its geometry and appearance. The differentiable 3D Gaussian Splatting framework is adapted to a dynamic object-centric setting. The input to the pipeline is a sequential set of RGB-D images. 3D reconstruction and 6-DoF pose tracking tasks are tackled using first-order gradient-based optimization. The formulation is simple, requires no pre-training, assumes no prior knowledge of the object or its motion, and is suitable for online applications. The proposed approach is validated on a dataset of 10 unknown spacecraft of diverse geometry and texture under arbitrary relative motion. The experiments demonstrate successful 3D reconstruction and accurate 6-DoF tracking of the target object in proximity operations over a short to medium duration. The causes of tracking drift are discussed and potential solutions are outlined.
通用感知是空间机器人高级自主性的支柱之一。在动态环境中估计未知物体的结构和运动对于这样的自主系统至关重要。传统上，这些解决方案依赖于目标物体的先验知识、多种不同的表示形式，或者不适合机器人操作的低保真输出。本工作提出了一种新颖的方法，使用统一表示——一组描述其几何形状和外观的三维高斯斑点——来增量重建和跟踪动态未知物体。差分三维高斯溅射框架被适应于动态物体中心的设置。该管线的输入是一组序列化的RGB-D图像。使用一阶梯度基优化来解决三维重建和6自由度姿态跟踪任务。该公式简单，无需预训练，不假设对物体或其运动的先验知识，并且适合在线应用。所提出的方法在一个包含10个具有不同几何和纹理的未知航天器在任意相对运动下的数据集上进行了验证。实验展示了目标物体在短到中期的近距离操作中成功的三维重建和准确的6自由度跟踪。讨论了跟踪漂移的原因，并概述了潜在的解决方案。