Under Pressure: Learning-Based Analog Gauge Reading in the Wild

We propose an interpretable framework for reading analog gauges that isdeployable on real world robotic systems. Our framework splits the reading taskinto 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 thescale and is able to extract the units used. We show that our gauge readingalgorithm is able to extract readings with a relative reading error of lessthan 2