19 citations · 31 across the 11 of their papers we have counts for
11 papers
Factorized Spatio-Temporal Convolutions for Human Pose Estimation from Planar Lidar
Simone Arreghini, Mirko Nava, Nicholas Carlotti +2
Localizing nearby humans and estimating their facing direction are key capabilities for safe navigation and socially aware human-robot interaction. Many pose-estimation pipelines t…
Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency
Nicholas Carlotti, Michele Antonazzi, Elia Cereda +4
We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated dat…
Self-supervised Learning Of Visual Pose Estimation Without Pose Labels By Classifying LED States
Nicholas Carlotti, Mirko Nava, Alessandro Giusti
We introduce a model for monocular RGB relative pose estimation of a ground robot that trains from scratch without pose labels nor prior knowledge about the robot's shape or appear…
Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar
Simone Arreghini, Nicholas Carlotti, Mirko Nava +2
Reliable localization of people is fundamental for service and social robots that must operate in close interaction with humans. State-of-the-art human detectors often rely on RGB-…
Multi-LED Classification as Pretext For Robot Heading Estimation
Nicholas Carlotti, Mirko Nava, Alessandro Giusti
We propose a self-supervised approach for visual robot detection and heading estimation by learning to estimate the states (OFF or ON) of four independent robot-mounted LEDs. Exper…
Learning to Estimate the Pose of a Peer Robot in a Camera Image by Predicting the States of its LEDs
Nicholas Carlotti, Mirko Nava, Alessandro Giusti
We consider the problem of training a fully convolutional network to estimate the relative 6D pose of a robot given a camera image, when the robot is equipped with independent cont…
Self-Supervised Learning of Visual Robot Localization Using LED State Prediction as a Pretext Task
Mirko Nava, Nicholas Carlotti, Luca Crupi +2
We propose a novel self-supervised approach for learning to visually localize robots equipped with controllable LEDs. We rely on a few training samples labeled with position ground…
Visual Servoing with Geometrically Interpretable Neural Perception
Antonio Paolillo, Mirko Nava, Dario Piga +1
An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, al…
Vision-State Fusion: Improving Deep Neural Networks for Autonomous Robotics
Elia Cereda, Stefano Bonato, Mirko Nava +2
Vision-based deep learning perception fulfills a paramount role in robotics, facilitating solutions to many challenging scenarios, such as acrobatic maneuvers of autonomous unmanne…
Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks
Mirko Nava, Antonio Paolillo, Jérôme Guzzi +2
We propose a general self-supervised learning approach for spatial perception tasks, such as estimating the pose of an object relative to the robot, from onboard sensor readings. T…
Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry
Mirko Nava, Jerome Guzzi, R. Omar Chavez-Garcia +2
We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor…