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Tactile recognition and localization using object models: the case of polyhedra on a plane
1Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; General Computers Company, Cambridge, MA 02139.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
This study shows how tactile sensor data can identify and locate objects using contact point positions and surface normal ranges. The algorithm efficiently prunes hypotheses for robust object recognition and localization.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Object recognition and localization are crucial in robotics.
- Tactile sensing offers rich information for object manipulation.
- Existing methods often require global information or complex feature extraction.
Purpose of the Study:
- To develop a method for object identification and localization using only local tactile sensor data.
- To enable robots to recognize and determine the position of objects from a known set.
- To leverage contact point and surface normal information for robust perception.
Main Methods:
- Utilizing local data from multiple tactile sensors, including contact point positions and surface normal ranges.
- Structuring the recognition and localization as a hypothesis generation and pruning process.
- Employing a tree-based search to manage consistent pairings between sensor contacts and object surfaces.
Main Results:
- The proposed algorithm demonstrates the feasibility of object recognition and localization using limited tactile information.
- Simulations show the effectiveness of the hypothesis pruning approach.
- The method is validated for polyhedral objects with three degrees of freedom on a known plane.
Conclusions:
- Local tactile sensor data is sufficient for effective object identification and localization.
- The hypothesis-driven approach provides a structured framework for tactile perception.
- This method contributes to more capable and adaptable robotic systems.
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