Riccardo Monica
email: rmonica[at]ce.unipr.it
mailing address:
Dip. di Ingegneria dell'Informazione Parco Area delle Scienze, 181A
43124 Parma, ITALY
lab phone: +39 0521 905702
Riccardo Monica is a Ph.D student at the Department of Information Engineering of the University of Parma. He received the Master degree (Laurea Magistrale) in 2014. His main research interests lie in robot perception of 3D environments.
Journal publications:
R. Monica, J. Aleotti, S. Caselli, A KinFu based approach for robot spatial attention and view planning, Robotics and Autonomous Systems, Volume 75, Part B, January 2016, Pages 627-640
Conference and Workshop Proceedings:
R. Monica, A. Zinelli, J. Aleotti, GMM-based Spatial Change Detection from Bimanual Tracking and Point Cloud Differences, Third Italian Workshop on Artificial Intelligence and Robotics (AIRO), Genoa, Italy, November 28, 2016.
R. Monica, J. Aleotti, S. Caselli, GMM-Based Detection of Human Hand Actions for Robot Spatial Attention, The 17th International Conference on Advanced Robotics (ICAR), Istanbul, Turkey, July 27-31, 2015.
J. Aleotti, D. Lodi Rizzini, R. Monica, S. Caselli, Global Registration of Mid-Range 3D Observations and Short Range Next Best Views, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, Illinois, USA, September 14-18, 2014. [Video]
Theses
Riccardo Monica, Automatic 3D scene reconstruction and update based on KinectFusion and active robot exploration, Master Thesis, University of Parma, 2014.
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Some open-source software can be found at his GitHub repository:
- basic_next_best_view: A ROS node for a simple next best view algorithm.
- gaussian_mixture_model: A ROS node to execute the EM algorithm on arbitrary data and obtain a Gaussian Mixture Model.
- ros_kinfu: PCL KinFu Large Scale wrapper for ROS.
- Kinect2 shadow removal: Fork of libfreenect2 with shadow removal filter, for OpenCL pipeline.
- rviz_cloud_annotation: Point cloud annotation tool, based on RViz.
Datasets:
- hand-polhemus-dataset: A dataset of hand trajectories acquired using the FasTrak Polhemus sensor.