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2 2013

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Sensory system calibration method for a walking robot

Łabęcki P., Belter D.

Journal of Automation Mobile Robotics and Intelligent Systems

2013

Vol. 7, No. 2

39--45

In this paper, a method for calibrating the sensory system of a walking robot is proposed. The robot is equipped with two exteroceptive sensors: a 2D laser scanner and a stereo camera. When using the CAD model of the robot, the positions of both sensors defined in the robot's body reference frame can only be determined with limited precision. Our goal is to create a method which allows the robot to find the position of the mounted sensors without the need for human input. The presented results show that the method is not only fast but also more precise than calibration using the CAD model.

Calibration of a rorating 2D laser range - finder using point - plane coistraints

Wai Yan So E., Basso F., Menegatti E.

Journal of Automation Mobile Robotics and Intelligent Systems

2013

Vol. 7, No. 2

30--38

A common method used to obtain 3D range data with a 2D laser range finder is to rotate the sensor. To combine the 2D range data obtained at different rotation angles into a common 3D coordinate frame, the axis of rotation relative to the mirror center of the laser range finder should be known. This axis of rotation is a line in 3D space with four degrees of freedom. This paper describes a method for recovering the parameters of this rotational axis, as well as the extrinsic calibration between the rota tional axis and a camera. It simply requires scanning several planar checkerboard patterns that are also imaged by a static camera. In particular, we use only correspondences between lines in the laser scans and planes in the camera images, which can be established easily even for non-visible lasers. Furthermore, we show that such line-on-plane correspondences can be modelled as pointplane constraints, a problem studied in the field of robot kinematics. We use a numerical solution developed for such point-plane constraint problems to obtain an initial estimate, which is then refined by a nonlinear minimiza- tion that minimizes the ,,line-of-sight" errors in the laser scans and the reprojection errors in the camera image. To validate our proposed method, we give experimental results using a LMS-100 mounted on a pan-tilt device in a nodding configuration.