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Michael Krainin

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3 papers

2 author rows

ICRA Conference 2012 Conference Paper

Exploiting segmentation for robust 3D object matching

Michael Krainin
Kurt Konolige
Dieter Fox

While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beambased models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.

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ICRA Conference 2011 Conference Paper

Autonomous generation of complete 3D object models using next best view manipulation planning

Michael Krainin
Brian Curless
Dieter Fox

Recognizing and manipulating objects is an important task for mobile robots performing useful services in everyday environments. In this paper, we develop a system that enables a robot to grasp an object and to move it in front of its depth camera so as to build a 3D surface model of the object. We derive an information gain based variant of the next best view algorithm in order to determine how the manipulator should move the object in front of the camera. By considering occlusions caused by the robot manipulator, our technique also determines when and how the robot should re-grasp the object in order to build a complete model.

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AAMAS Conference 2011 Conference Paper

Effective Variants of Max-Sum Algorithm to Radar Coordination and Scheduling

Yoonheui Kim
Michael Krainin
Victor Lesser

This work proposes new techniques for saving communication and computational resources when solving distributed constraint optimization problems in an environment where system hardware resources are clustered. Using a pre-computed policy and two phase propagation on Max-Sum algorithm, the system performance on Radar scheduling problem improves in terms of communication and computation.

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