Monocular navigation for long-term autonomy

We present a reliable and robust monocular navigation system for an autonomous vehicle. The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS. Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach. In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled. We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound. The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes. This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation

FPGA-based module for SURF extraction

We present a complete hardware and software solution of an FPGA-based computer vision embedded module capable of carrying out SURF image features extraction algorithm. Aside from image analysis, the module embeds a Linux distribution that allows to run programs specifically tailored for particular applications. The module is based on a Virtex-5 FXT FPGA which features powerful configurable logic and an embedded PowerPC processor. We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. The module's overall performance is evaluated and compared to CPU and GPU based solutions. Results show that the embedded module achieves comparable disctinctiveness to the SURF software implementation running in a standard CPU while being faster and consuming significantly less power and space. Thus, it allows to use the SURF algorithm in applications with power and spatial constraints, such as autonomous navigation of small mobile robots

Decision support system for hot spot detection

Postprint (published version

Monocular navigation for long-term autonomy

We present a reliable and robust monocular navigation system for an autonomous vehicle.The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS.Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach.In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled.We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound.The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes.This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation.</p

A Behavior-based approach for educational robotics activities

Educational robotics proposes the use of robots as a teaching resource that enables inexperienced students to approach topics in fields unrelated to robotics. In recent years, these activities have grown substantially in elementary and secondary school classrooms and also in outreach experiences to interest students in science, technology, engineering, and math (STEM) undergraduate programs. A key problem in educational robotics is providing a satisfactory, adequate, easy-to-use interface between an inexpert public and the robots. This paper presents a behavior-based application for programming robots and the design of robotic-centered courses and other outreach activities. Evaluation data show that over 90% of students find it easy to use. These activities are part of a comprehensive outreach program conducted by the Exact and Natural Science Faculty of the University of Buenos Aires, Argentina (FCEN-UBA). Statistical data show that since 2009 over 35% of new students at the FCEN-UBA have participated in some outreach activity, suggesting their significant impact on student enrollment in STEM-related programs.Fil: de Cristóforis, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pedre, Sol. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nitsche, Matias Alejandro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fischer, Thomas. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pessacg, Facundo Hugo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Pietro, Carlos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentin

Decision support system for hot spot detection

Postprint (published version

Decision support system for hot spot detection

Forest fires are an important problem for many countries. Prevention, surveillance and extinguishing tasks are costly and sometimes risky. This paper focuses on the surveillance of the fire embers once the fire is extinguished. This is a post-fire task which requires an important amount of terrestrial resources, especially if the weather conditions may provoke the re-ignition of the fire. We propose the use of an Unmanned Aerial System to support fire fighters’ decisions. Our proposal is to develop an intelligent system which can make or suggest tactical decisions to firemen on hot-spot surveillance. The Unmanned Aerial System consists of a small unmanned aircraft, a ground control station and several personal devices for the firemen on ground. All system elements are wirelessly connected. On board data acquisition and fast processing, together with an intelligent transmission and interpretation are the targets of the system