  Mobile service robots are increasingly prevalent in human-centric, real-world
domains, operating autonomously in unconstrained indoor environments. In such a
context, robotic vision plays a central role in enabling service robots to
perceive high-level environmental features from visual observations. Despite
the data-driven approaches based on deep learning push the boundaries of vision
systems, applying these techniques to real-world robotic scenarios presents
unique methodological challenges. Traditional models fail to represent the
challenging perception constraints typical of service robots and must be
adapted for the specific environment where robots finally operate. We propose a
method leveraging photorealistic simulations that balances data quality and
acquisition costs for synthesizing visual datasets from the robot perspective
used to train deep architectures. Then, we show the benefits in qualifying a
general detector for the target domain in which the robot is deployed, showing
also the trade-off between the effort for obtaining new examples from such a
setting and the performance gain. In our extensive experimental campaign, we
focus on the door detection task (namely recognizing the presence and the
traversability of doorways) that, in dynamic settings, is useful to infer the
topology of the map. Our findings are validated in a real-world robot
deployment, comparing prominent deep-learning models and demonstrating the
effectiveness of our approach in practical settings.
