Invariant representations to reduce the variability in recognition of rigid body motion trajectories

Delabie, Tjorven; Cigdem, Ozkan; Schutter, Jochem F.M. De; Matthysen, Roel; Laet, Tinne De; Schutter, Joris De

doi:10.1109/icsmc.2012.6377975

Cited by 2 publications

(1 citation statement)

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“…The pipeline for recognizing motions starting from the measurements was extensively explained in [16]. The three main differences here are that a new type of trajectory descriptor is considered (the extended Frenet-Serret invariants), that not only the screw axis-based invariants are made dimensionless, and that the effect of variations in the choice of reference point are studied.…”

Section: Motion Recognition Experimentsmentioning

confidence: 99%

Comparison of rigid body motion trajectory descriptors for motion representation and recognition

Vochten

Laet

Schutter

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an overview and comparison of minimal and complete rigid body motion trajectory descriptors, usable in applications like motion recognition and programming by demonstration. Motion trajectory descriptors are able to deal with potentially unwanted variations acting on the motion trajectory such as changes in the execution time, the motion's starting position, or the viewpoint from which the motion is observed. A suitable rigid body motion trajectory descriptor retains only the trajectory information relevant to the application. This paper compares different trajectory descriptors for rigid body motion and validates their usefulness for dealing with motion variation in a motion recognition experiment. Furthermore, a new type of invariant trajectory descriptor is introduced based on the Frenet-Serret formulas.

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Section: Motion Recognition Experimentsmentioning

confidence: 99%