Stefano Rosa

Multi-robot Map Updating in Dynamic Environments

Multi-robot systems play an important role in many robotic applications. A prerequisite for a team of robots is the capability of building and maintaining updated maps of the environment. The simultaneous estimation of the trajectory and the map of the environment (known as SLAM) requires many computational resources. Moreover, SLAM is generally performed in environments that do not vary over time (called static environments), whereas real applications commonly require navigation services in dynamic environments. This paper focuses on long term mapping operativity in presence of variations in the map, as in the case of robotic applications in logistic spaces, where rovers have to track the presence of goods in given areas. In this context classical SLAM approaches are generally not directly applicable, since they usually apply in static environments or in dynamic environments where it is possible to model the environment dynamics. This paper proposes a methodology that allows the robots to detect variations in the environment, generate maps containing only the persistent variations, propagate thiem to the team and finally merge the received information in a consistent way. The team of robots is also exploited to assure the coverage of areas not visited for long time, thus improving the knowledge on the present status of the map. The map updating process is demonstrated to be computationally light, in order to be performed in parallel with other tasks (e.g., team coordination and planning, surveillance).

Supervision and monitoring of logistic spaces by a cooperative robot team: methodologies, problems, and solutions

Mobile robots can be employed in the logistic field to efficiently perform common tasks, such as building and updating maps of indoor and outdoor logistic spaces, locating specific goods on the map, tracing the product flow in the area, while preserving situational awareness and safety of the environment. This paper reports and discusses the main results of the MACP4Log (Mobile Autonomous and Cooperating robotic Platforms for supervision and monitoring of large LOGistic surfaces) research project, aimed at the study and development of a set of algorithms and services, enabling autonomous navigation of a team of mobile robots in large logistic spaces, and exploiting cooperation, through communication with a supervisor and among the robotic platforms. Although the main services required for the robots coincide with the most common issues of mobile robotics (i.e., localization, mapping, SLAM and exploration), the particular characteristics of the logistic spaces introduce specific problems (e.g., related to a high symmetry of the environment and/or to its variability), which must be properly taken into account. The paper discusses in detail such problems, summarizing the main results achieved both from the methodological and the experimental standpoint, and is completed by the description of the general functional architecture of the whole system, including navigation, logistic, and monitoring services.

Fly4SmartCity: A cloud robotics service for smart city applications

In this paper a possible implementation of a cloud-robotics service in a smart city scenario is proposed, in which small Unmanned Aerial Vehicles (UAVs) are employed for emergency management operations. The emerging cloud robotics paradigm considers robots as simple agents connected to a remote network infrastructure; this networked approach allows them to benefit from off-board powerful computational and storage resources on one hand, and to leverage open data about the urban fabric and common knowledge on the other hand. This work describes how the integration of a capable, reliable and stable cloud platform, small unmanned vehicles, high-bandwidth/low-latency mobile networks and open data publicly available on the internet makes the design of a real-world UAV-based emergency management service possible. Simulated and real experiments show the feasibility of the approach. Legal and operational issues which at present still prevent a practical application of these techniques are also addressed.

A cloud robotics system for telepresence enabling mobility impaired people to enjoy the whole museum experience

We present a novel robotic telepresence platform composed by a semi-autonomous mobile robot based on a cloud robotics framework, which has been developed with the aim of enabling mobility impaired people to enjoy museums and archaeological sites that would be otherwise inaccessible. Such places, in fact, very often are not equipped to provide access for mobility impaired people, in particular because these aids require dedicated infrastructures that may not fit within the environment and large investments. For this reason, people affected by mobility impairments are often unable to enjoy a part or even the entire museum experience. Solutions allowing mobility impaired people to enjoy museum experience are often based on recorded tours, thus they do not allow active participation of the user. On the contrary, the presented platform is intended to allow users to enjoy completely the museum round. A robot equipped with a camera is placed within the museum and users can control it in order to follow predefined tours or freely explore the museum. Our solution ensures that users see exactly what the robot is seing in real-time. The cloud robotics platform controls both navigation capabilities and teleoperation. Navigation tasks are intended to let the robot reliably follow pre-defined tours, while main concern of teleoperation tasks is to ensure robot safety (e.g., by means of dynamic obstacle detection and avoidance software). Proposed platform has been optimized to maximize user experience.

Skeleton Tracking Based Complex Human Activity Recognition Using Kinect Camera

This paper presents a new and efficient algorithm for complex human activity recognition using depth videos recorded from a single Microsoft Kinect camera. The algorithm has been implemented on videos recorded from Kinect camera in OpenNI video file format (.oni). OpenNI file format provides a combined video with both RGB and depth information. An OpenNI specific dataset of such videos has been created containing 200 videos of 8 different activities being performed by different individuals. This dataset should serve as a reference for future research involving OpenNI skeleton tracker. The algorithm is based on skeleton tracking using state of the art OpenNI skeleton tracker. Various joints and body parts in human skeleton have been tracked and the selection of these joints is made based on the nature of the activity being performed. The change in position of the selected joints and body parts during the activity has been used to construct feature vectors for each activity. Support vector machine (SVM) multi-class classifier has been used to classify and recognize the activities being performed. Experimental results show the algorithm is able to successfully classify the set of activities irrespective of the individual performing the activities and the position of the individual in front of the camera.

A Cloud Based Service for Management and Planning of Autonomous UAV Missions in Smart City Scenarios

Cloud Robotics is an emerging paradigm in which robots, seen as abstract agents, have the possibility to connect to a common network and share on a complex infrastructure the information and knowledge they gather about the physical world; or conversely consume the data collected by other agents or made available on accessible database and repositories. In this paper we propose an implementation of an emergency-management service exploiting the possibilities offered by cloud robotics in a smart city scenario. A high-level cloud-platform manages a number of unmanned aerial vehicles (quadrotor UAVs) with the goal of providing aerial support to citizens that require it via a dedicated mobile app. The UAV reaches the citizen while forwarding a real-time video streaming to a privileged user (police officer),connected to the same cloud platform, that is allowed to teleoperate it by remote.

Graph optimization with unstructured covariance: fast, accurate, linear approximation

This manuscript addresses the problem of optimization- based Simultaneous Localization and Mapping (SLAM), which is of concern when a robot, traveling in an unknown environment, has to build a world model, exploiting sensor measurements. Although the optimization problem underlying SLAM is nonlinear and nonconvex, related work showed that it is possible to compute an accurate linear approximation of the optimal solution for the case in which measurement covariance matrices have a block diagonal structure. In this paper we relax this hypothesis on the structure of measurement covariance and we propose a linear approximation that can deal with the general unstructured case. After presenting our theoretical derivation, we report an experimental evaluation of the proposed technique. The outcome confirms that the technique has remarkable advantages over state-of-the-art approaches and it is a promising solution for large-scale mapping.

Leveraging Open Data for Supporting a Cloud Robotics Service in a Smart City Environment

At the dawn of cloud robotics , one of the biggest challenges is to successfully exploit the power offered by the internet for acquiring and sharing information, in order to build a common knowledge base among the agents. In this paper , we propose a cloud robotics service for emergency management in a smart city scenario. The application is capable of collecting significant open data over the internet. Then it uses them in the mission planning phase, in order to autonomously define a set of waypoints toward the target coordinates.

Sliding Autonomy in Cloud Robotics Services for Smart City Applications

The aim of this paper is to present a sliding autonomy approach for Unmanned Aerial Vehicles (UAVs) in the context of the project Fly4SmartCity. The project consists in the implementation of a cloud robotics service in which small UAVs are employed for emergency management, monitoring and surveillance in a smart city scenario. Human-robot interaction is mediated by the cloud robotics platform. We imagine three main levels of autonomy for UAVs: full autonomy, mixed-initiative and teleoperation. Then we propose different scenarios in which we analyze the Level Of Autonomy and the sliding autonomy approach. All services use shared knowledge (crowdsourcing and other data sources available on the Internet) for the management and control of the UAVs.

Real-Time Single Camera Hand Gesture Recognition System for Remote Deaf-Blind Communication

This paper presents a fast approach for marker-less Full-DOF hand tracking, leveraging only depth information from a single depth camera. This system can be useful in many applications, ranging from tele-presence to remote control of robotic actuators or interaction with 3D virtual environment. We applied the proposed technology to enable remote transmission of signs from Tactile Sing Languages (i.e., Sign Languages with Tactile feedbacks), allowing non-invasive remote communication not only among deaf-blind users, but also with deaf, blind and hearing with proficiency in Sign Languages. We show that our approach paves the way to a fluid and natural remote communication for deaf-blind people, up to now impossible. This system is a first prototype for the PARLOMA project, which aims at designing a remote communication system for deaf-blind people.