  Many mobile robotic platforms rely on an accurate knowledge of the extrinsic
calibration parameters, especially systems performing visual stereo matching.
Although a number of accurate stereo camera calibration methods have been
developed, which provide good initial "factory" calibrations, the determined
parameters can lose their validity over time as the sensors are exposed to
environmental conditions and external effects. Thus, on autonomous platforms
on-board diagnostic methods for an early detection of the need to repeat
calibration procedures have the potential to prevent critical failures of
crucial systems, such as state estimation or obstacle detection. In this work,
we present a novel data-driven method to estimate the calibration quality and
detect discrepancies between the original calibration and the current system
state for stereo camera systems. The framework consists of a novel dataset
generation pipeline to train CalQNet, a deep convolutional neural network.
CalQNet can estimate the calibration quality using a new metric that
approximates the degree of miscalibration in stereo setups. We show the
framework's ability to predict from a single stereo frame if a state-of-the-art
stereo-visual odometry system will diverge due to a degraded calibration in two
real-world experiments.
