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W E Grimson1, T Lozano-Perez

  • 1M.I.T. Artificial Intelligence Laboratory, 545 Technology Square, Cambridge, MA 02139.

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Summary
This summary is machine-generated.

This study presents a method for identifying and locating overlapping polyhedral objects using local sensor data. It efficiently discards incorrect object position hypotheses using geometric constraints.

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Area of Science:

  • Robotics and Computer Vision
  • Geometric Computing
  • Sensor Data Analysis

Background:

  • Object recognition and localization are crucial in robotics and automated systems.
  • Handling overlapping objects presents a significant challenge in perception tasks.
  • Previous methods focused on non-overlapping object scenarios.

Purpose of the Study:

  • To develop a method for identifying and locating overlapping polyhedral objects using local sensor measurements.
  • To extend existing object recognition techniques to handle occluded and overlapping scenarios.
  • To leverage geometric constraints for efficient hypothesis verification.

Main Methods:

  • Modeling objects as polyhedra with up to six degrees of freedom.
  • Generating and evaluating hypotheses for pairings between sensed data and object surfaces.
  • Utilizing local geometric constraints including distances between faces and angles between surface normals.
  • Employing constraints on vectors between sensed points relative to surface normals.

Main Results:

  • Successfully demonstrated the identification and localization of overlapping polyhedral objects.
  • Efficiently pruned inconsistent object pose hypotheses using local geometric constraints.
  • The method extends prior work on non-overlapping object recognition and localization.

Conclusions:

  • Local sensor measurements of positions and surface normals are effective for identifying and locating overlapping objects.
  • The proposed hypothesis-testing framework with geometric constraints provides an efficient solution for complex object perception.
  • This approach advances the capabilities of robotic systems in cluttered environments.