Maurilio Di Cicco

2016

Unsupervised calibration of wheeled mobile platforms


Maurilio Di Cicco, Bartolomeo Della Corte, Giorgio Grisetti


This paper describes an unsupervised approach to retrieve the kinematic parameters of a wheeled mobile robot.

The robot chooses which action to take in order to minimize the uncertainty in the parameter estimate and to fully explore the parameter space. Our method explores the effects of a set of elementary motion on the platform to dynamically select the best action and to stop the process when the estimate can be no further improved. We tested our approach both in simulation and with real robots.

Our method is reported to obtain in shorter time parameter estimates that are statistically more accurate than the ones obtained by steering the robot on predefined patterns.

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2015

Non-Parametric Calibration for Depth Sensors (journal paper)


Maurilio Di Cicco, Luca Iocchi, Giorgio Grisetti


RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstruction of the environment and mapping. In these tasks, uncalibrated sensors can generate poor quality results.

In this article we propose a quick and easy to use approach to estimate the undistortion function of RGBD sensors. Our approach does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute an extensive representation of the undistortion function as well as its statistics and use machine learning methods for approximation of the undistortion function.

We validated our approach on datasets acquired from different kinds of RGBD sensors and using a precise 3D ground truth. We also provide a procedure for evaluating the quality of the calibration using a mobile robot and a 2D laser range finder. The results clearly show the advantages in using sensor data calibrated with the method described in this article.

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2014

Non-Parametric Calibration for Depth Sensors


Maurilio Di Cicco, Luca Iocchi, Giorgio Grisetti


In this paper we propose a quick and easy approach to estimate the undistortion function of RGBD sensors. Our method does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute a nonparametric approximation of the undistortion function by applying regression methods to calibration data that can be acquired wherever a sufficiently large planar surface is observed. The procedure is fast, easy and be used on-line. Experimental results show a significant improvement when using undistorted images in applications like mapping.

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2013

Exploration and rovina_paperMapping of Catacombs with Mobile Robots


Ziparo V. A.; Zaratti, M.; Grisetti, G.; Bonanni, T.M.; Serafin, J.; Di Cicco, M.; Proesmans, M.; Van Gool, L.; Vysotska, O.; Bogoslavskyi, I.; Stachniss, C.


ROVINA project (http://rovina-project.eu) is a FP7 project recently funded by the EC that focusses on the exploration, digital preservation, and visualization of archeological sites. Its key objectives are:

- developing autonomous robots for creating digital models of hard-to-access environments
- improving autonomous navigation for robots exploring unknown underground environments
- building large 3D textured models of poorly structured environments

- offering a cost-effective support for performing continuous monitoring of these sites and to enable comparative analysis that will allow to devise better preservation plans.

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