M
Matia Pizzoli
Researcher at Sapienza University of Rome
Publications - 18
Citations - 2768
Matia Pizzoli is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Speaker recognition & Visual search. The author has an hindex of 10, co-authored 18 publications receiving 2259 citations. Previous affiliations of Matia Pizzoli include University of Zurich.
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Proceedings ArticleDOI
SVO: Fast semi-direct monocular visual odometry
TL;DR: A semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods and applied to micro-aerial-vehicle state-estimation in GPS-denied environments is proposed.
Proceedings ArticleDOI
REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time
TL;DR: This work proposes a novel approach to depth map computation that combines Bayesian estimation and recent development on convex optimization for image processing, and demonstrates that this method outperforms state-of-the-art techniques in terms of accuracy.
Journal ArticleDOI
Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle
Matthias Faessler,Flavio Fontana,Christian Forster,Elias Mueggler,Matia Pizzoli,Davide Scaramuzza +5 more
TL;DR: A vision‐based quadrotor micro aerial vehicle that can autonomously execute a given trajectory and provide a live, dense three‐dimensional map of an area and the practical challenges and lessons learned are discussed.
Proceedings ArticleDOI
Rescue robots at earthquake-hit Mirandola, Italy: A field report
G-J M. Kruijff,Viatcheslav Tretyakov,Thorsten Linder,Fiora Pirri,Mario Gianni,Panagiotis Papadakis,Matia Pizzoli,Arnab Sinha,E. Pianese,Salvatore Corrao,F. Priori,S. Febrini,S. Angeletti +12 more
TL;DR: NIFTi deployed a team of humans and robots (UGV, UAV) in the red-area of Mirandola, Emilia-Romagna, from Tuesday July 24 until Friday July 27, 2012, to assess damage to historical buildings, and cultural artifacts located therein.
Proceedings ArticleDOI
Air-ground localization and map augmentation using monocular dense reconstruction
TL;DR: A novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement is presented, which achieves high accuracy whereas appearance-based, state-of-the-art approaches fail.