Under pressure: learning-based analog gauge reading in the wild
Maurits Reitsma Julian Keller Kenneth Blomqvist Roland Siegwart
We propose an interpretable framework for reading analog gauges that is
deployable on real world robotic systems. Our framework splits the reading task
into distinct steps, such that we can detect potential failures at each step.
Our system needs no prior knowledge of the type of gauge or the range of the
scale and is able to extract the units used. We show that our gauge reading
algorithm is able to extract readings with a relative reading error of less
than 2%.