TY  - JOUR
AB  - In-hand object localization has always been a
critical but difficult aspect of dexterous robotic manipulation.
We attempt to address this issue in this paper through the use
of point cloud registration techniques. Specifically, the grasping
pose is estimated by registering the high-resolution 3D contact
point cloud sensed by a novel GelStereo tactile sensor with the
object template point cloud. Extensive qualitative and quantita-
tive analyses of in-hand localization and insertion experiments
of small parts are performed on our robot platform. The
experimental results verify the accuracy and robustness of the
proposed in-hand object localization pipeline.
DA  - 2021
LA  - eng
PY  - 2021
TI  - In-Hand Object Localization Using GelStereo Visuotactile Sensing
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29606361
Y2  - 2024-11-22T00:42:10
ER  -