发表机构
University of Technology Nuremberg; Lund University（纽伦堡工业大学; 隆德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于紧凑位姿图优化的轻量级跟踪模块，将其应用于多种对应估计方法，实现了低成本、高精度的刚体长时6D位姿跟踪。
AI 中文摘要
在长序列中跟踪新物体的6D位姿，当前要么需要昂贵的初始化操作，要么需要在整个序列中维护重建结果，这使得现有跟踪器无法适用于机器人操纵和增强现实领域，而这些领域需要可直接使用且能实时运行的跟踪器。本文展示了一种轻量级跟踪模块，可应用于多种对应估计方法之上，以将漂移限制在一定范围内，同时保持快速运行时间。我们的核心思路是避免在位姿图中进行基于点的优化，仅基于相对位姿约束进行操作，并通过几何对齐得到的不确定性对这些约束进行加权。这使得优化过程与对应数量无关，同时避免直接引入含噪的点测量值，从而实现快速且鲁棒的长时跟踪。在四个真实世界基准测试中，我们的方法达到了与基于重建的跟踪器相当的跟踪精度，而优化成本仅为其一小部分。总体而言，这些结果表明，紧凑且可靠的位姿图优化能以显著更低的计算成本提供长序列一致性。
英文摘要
Tracking a novel object's 6D pose over long horizons currently requires either expensive onboarding or a reconstruction maintained throughout the sequence. This makes current trackers impractical for robotic manipulation and augmented reality, which need trackers that are ready to use and run in real time. We show that a lightweight tracking module can be applied on top of a wide range of correspondence estimation methods to keep drifts bounded while maintaining fast runtime. Our key idea is to avoid point-based optimization in the pose graph and operate only on relative pose constraints, which we weight by a derived uncertainty from the geometric alignment. This makes optimization independent of the number of correspondences while avoiding the direct inclusion of noisy point measurements, leading to fast and robust long-term tracking. Across four real-world benchmarks, our approach achieves tracking accuracy comparable to reconstruction-based trackers with a fraction of the optimization cost. Overall, these results suggest that a compact and reliable pose graph optimization can provide long-horizon consistency at substantially lower computational cost.
