  Self-diagnosis and self-repair are some of the key challenges in deploying
robotic platforms for long-term real-world applications. One of the issues that
can occur to a robot is miscalibration of its sensors due to aging,
environmental transients, or external disturbances. Precise calibration lies at
the core of a variety of applications, due to the need to accurately perceive
the world. However, while a lot of work has focused on calibrating the sensors,
not much has been done towards identifying when a sensor needs to be
recalibrated. This paper focuses on a data-driven approach to learn the
detection of miscalibration in vision sensors, specifically RGB cameras. Our
contributions include a proposed miscalibration metric for RGB cameras and a
novel semi-synthetic dataset generation pipeline based on this metric.
Additionally, by training a deep convolutional neural network, we demonstrate
the effectiveness of our pipeline to identify whether a recalibration of the
camera's intrinsic parameters is required or not. The code is available at
http://github.com/ethz-asl/camera_miscalib_detection.
