Enhancing motion trajectory segmentation of rigid bodies using a novel screw-based trajectory-shape representation
This work addresses trajectory segmentation for rigid bodies in robotics or motion analysis, offering incremental improvements in invariance and robustness.
The paper tackled the problem of 3D rigid-body motion trajectory segmentation by proposing a novel screw-based representation that incorporates both translation and rotation, showing more robust detection of submotions and consistent segmentation compared to conventional methods in simulations and real human pouring motions.
Trajectory segmentation refers to dividing a trajectory into meaningful consecutive sub-trajectories. This paper focuses on trajectory segmentation for 3D rigid-body motions. Most segmentation approaches in the literature represent the body's trajectory as a point trajectory, considering only its translation and neglecting its rotation. We propose a novel trajectory representation for rigid-body motions that incorporates both translation and rotation, and additionally exhibits several invariant properties. This representation consists of a geometric progress rate and a third-order trajectory-shape descriptor. Concepts from screw theory were used to make this representation time-invariant and also invariant to the choice of body reference point. This new representation is validated for a self-supervised segmentation approach, both in simulation and using real recordings of human-demonstrated pouring motions. The results show a more robust detection of consecutive submotions with distinct features and a more consistent segmentation compared to conventional representations. We believe that other existing segmentation methods may benefit from using this trajectory representation to improve their invariance.