English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Trajectory segmentation refers to dividing a trajectory into meaningfulconsecutive sub-trajectories. This paper focuses on trajectory segmentation for3D rigid-body motions. Most segmentation approaches in the literature representthe body's trajectory as a point trajectory, considering only its translationand neglecting its rotation. We propose a novel trajectory representation forrigid-body motions that incorporates both translation and rotation, andadditionally exhibits several invariant properties. This representationconsists of a geometric progress rate and a third-order trajectory-shapedescriptor. Concepts from screw theory were used to make this representationtime-invariant and also invariant to the choice of body reference point. Thisnew representation is validated for a self-supervised segmentation approach,both in simulation and using real recordings of human-demonstrated pouringmotions. The results show a more robust detection of consecutive submotionswith distinct features and a more consistent segmentation compared toconventional representations. We believe that other existing segmentationmethods may benefit from using this trajectory representation to improve theirinvariance.

Enhancing motion trajectory segmentation of rigid bodies using a novel screw-based trajectory-shape representation