Obstacle modeling for manipulator using iterative least square (ILS) and iterative closest point (ICP) base on Kinect

Obstacle modeling for manipulator using iterative least square (ILS) and iterative closest point (ICP) base on Kinect

This paper presents a method to distinguish between a manipulator and its surroundings using a depth sensor. The depth sensor used is Kinect. First Kinect calibration is addressed. Then coordinate calibration between Kinect and the manipulator are solved using iterative least square (ILS) algorithm....

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Hauptverfasser: Sukmanee, W., Ruchanurucks, M., Rakprayoon, P.
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Rakprayoon, P.
description This paper presents a method to distinguish between a manipulator and its surroundings using a depth sensor. The depth sensor used is Kinect. First Kinect calibration is addressed. Then coordinate calibration between Kinect and the manipulator are solved using iterative least square (ILS) algorithm. At this point, to delete the robot from the scene and keep only the surrounding surface, the accuracy of homogeneous transformation acquired from ILS is inadequate. We further focus on a matching method between the manipulator's model and point cloud, to use iterative closest point (ICP) algorithm. ICP enhances the accuracy for a great deal. Experiment shows that this comprehensive method is practical and robust. It can be used in dynamic environment as well.
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title Obstacle modeling for manipulator using iterative least square (ILS) and iterative closest point (ICP) base on Kinect
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