MotionDLO: Hybrid Event- and Frame-Based Tracking of Deformable Linear Objects

2026-08-23

AI Summary

本文提出MotionDLO，一种结合事件和帧的混合跟踪框架，用于解决变形线性物体实时跟踪中的鲁棒性、准确性和时间一致性问题。

Abstract

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.

Problem

• deformable linear objects
• robot perception
• temporal continuity
• robustness
• accuracy

Innovation

• Hybrid Event- and Frame-Based Tracking
• Deformable Linear Objects
• Motion Coherence Theory
• Coherent Point Drift (CPD)
• Real-time Performance