MotionDLO：基于事件与帧的混合可变形线性物体跟踪框架

中文摘要 AI 辅助

可靠跟踪运动中的可变形线性物体（DLO），同时确保鲁棒性、准确性和时间一致的状态估计，仍是机器人感知领域的基础挑战。我们提出MotionDLO，这是一个专门设计用于克服时间连续性和延迟限制的实时跟踪框架。该方法利用事件相机的高时间分辨率和稀疏性，结合分割技术与相干点漂移（CPD）算法，遵循运动一致性理论的原则。这种集成实现了时间一致的形状估计，同时保持较低的计算开销。现有的基于事件的跟踪方法通常计算效率高，但与基于帧的方法相比准确性降低，或者为达到有竞争力的性能而牺牲事件稀疏性。为解决这种权衡，我们提出一种基于事件与帧的混合跟踪架构，保留两种传感模态的互补优势：事件流确保高频运动更新，而基于帧的信息稳定空间准确性和物体身份。我们证明所提出的框架能可靠关联视频序列中的DLO实例，为机器人操作任务提供鲁棒感知。实验结果验证其以12ms的更新速率实现实时性能，且以点到曲线误差（作为准确性度量）最高达0.43mm实现准确的形状跟踪，支持操作过程中的动态路径适配。源代码和演示数据集公开可用。

英文摘要

Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimation remains a fundamental challenge in robot perception. We introduce MotionDLO, a real-time tracking framework specifically designed to overcome these limitations in temporal continuity and latency. The method exploits the high temporal resolution and sparsity of event-based cameras and combines segmentation with the Coherent Point Drift (CPD) algorithm under the principles of Motion Coherence Theory. This integration enables temporally consistent shape estimation while maintaining a low computational overhead. Existing event-based tracking methods are typically computationally efficient but exhibit reduced accuracy compared to frame-based approaches, or alternatively compromise event sparsity to achieve competitive performance. To resolve this trade-off, we propose a hybrid event- and frame-based tracking architecture that preserves the complementary strengths of both sensing modalities. The event stream ensures high-frequency motion updates, while frame-based information stabilizes spatial accuracy and object identity. We demonstrate that the proposed framework reliably associates DLO instances across video sequences, enabling robust perception for robotic manipulation tasks. Experimental results validate real-time performance at 12 ms update rates and accurate shape tracking with an point-to-curve error as measurement of accuracy of up to 0.43 mm, supporting dynamic path adaptation during manipulation. The source code and demonstration datasets are publicly available.

发表机构

• Institute for Factory Automation and Production Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学工厂自动化与生产系统研究所）

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