Data fusion in ubiquitous networked robot systems for urban services
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Mostrar el registro completo del ítemAutoría
Merino, Luis




Palabras clave
Ubiquitous networked robotsDecentralized data fusion
Service and social robotics
Person tracking
Proyecto FROG
Fecha de publicación
2012-06-16Resumen
There is a clear trend in the use of robots
to accomplish services that can help humans. In this
paper, robots acting in urban environments are considered
for the task of person guiding. Nowadays, it is
common to have ubiquitous sensors integrated within
the buildings, such as camera networks, and wireless
communications like 3G or WiFi. Such infrastructure
can be directly used by robotic platforms. The paper
shows how combining the information from the robots
and the sensors allows tracking failures to be overcome,
by being more robust under occlusion, clutter, and
lighting changes. The paper describes the algorithms
for tracking with a set of fixed surveillance cameras
and the algorithms for position tracking using the signal
strength received by a wireless sensor network (WSN).
Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is
described. The estimate from all these sources are then
combined using a decentrali ...
There is a clear trend in the use of robots
to accomplish services that can help humans. In this
paper, robots acting in urban environments are considered
for the task of person guiding. Nowadays, it is
common to have ubiquitous sensors integrated within
the buildings, such as camera networks, and wireless
communications like 3G or WiFi. Such infrastructure
can be directly used by robotic platforms. The paper
shows how combining the information from the robots
and the sensors allows tracking failures to be overcome,
by being more robust under occlusion, clutter, and
lighting changes. The paper describes the algorithms
for tracking with a set of fixed surveillance cameras
and the algorithms for position tracking using the signal
strength received by a wireless sensor network (WSN).
Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is
described. The estimate from all these sources are then
combined using a decentralized data fusion algorithm
to provide an increase in performance. This scheme is
scalable and can handle communication latencies and
failures. We present results of the system operating in
real time on a large outdoor environment, including 22
nonoverlapping cameras, WSN, and several robots.