Context-based Selection and Execution of Robot Perception Graphs

To perform a wide range of tasks service robots
need to robustly extract knowledge about the world from the
data perceived through the robot’s sensors even in the presence
of varying context-conditions. This makes the design and devel-
opment of robot perception architectures a challenging exercise.
In this paper we propose a robot perception architecture which
enables to select and execute at runtime different perception
graphs based on monitored context changes. To achieve this
the architecture is structured as a feedback loop and contains
a repository of different perception graph configurations suitable
for various context conditions.