A Probabilistic Approach to Tactile Shape Reconstruction

In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point...

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Bibliographic Details
Published in:IEEE transactions on robotics 2011-06, Vol.27 (3), p.630-635
Main Authors: Meier, M, Schopfer, M, Haschke, R, Ritter, H
Format: Article
Language:English
Subjects:
3-D reconstruction
3-D technology
Algorithms
Clouds
Construction
Grasping
Iterative closest point algorithm
Iterative methods
Kalman filters
Manganese
Probabilistic methods
Probability theory
recognition
Representations
Sensors
Shape
Tactile
tactile sensing
Tactile sensors
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Summary:In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point cloud that is efficiently stored in a kd-tree. The quality of generated shape representations is compared with a naive averaging approach, and we show that our method provides superior accuracy. We also evaluate the feasibility of object classification combining the generated object representations, together with the iterative closest point algorithm.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2011.2120830