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Alberto Pretto
Researcher at Sapienza University of Rome
Publications - 70
Citations - 1360
Alberto Pretto is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 18, co-authored 63 publications receiving 975 citations. Previous affiliations of Alberto Pretto include University of Padua.
Papers
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Proceedings ArticleDOI
A Robust and Easy to Implement Method for IMU Calibration without External Equipments
TL;DR: A robust and quick calibration protocol that exploits an effective parameterless static filter to reliably detect the static intervals in the sensor measurements, where it is assumed local stability of the gravity's magnitude and stable temperature.
Book ChapterDOI
Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture
TL;DR: A novel unsupervised dataset summarization algorithm that automatically selects from a large dataset the most informative subsets that better describe the original one enables to streamline and speed-up the manual dataset labeling process, otherwise extremely time consuming, while preserving good classification performance.
Journal ArticleDOI
Omnidirectional vision scan matching for robot localization in dynamic environments
TL;DR: An omnidirectional camera mounted on a mobile robot is used to perform a sort of scan matching, which finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles.
Proceedings ArticleDOI
A visual odometry framework robust to motion blur
TL;DR: A new feature detection and tracking scheme that is robust even to non-uniform motion blur is proposed and a framework for visual odometry based on features extracted out of and matched in monocular image sequences is developed.
Proceedings ArticleDOI
Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation
TL;DR: A deep learning based method to allow a robot to perform an accurate weed/crop classification using a sequence of two Convolutional Neural Networks applied to RGB images is described.