Under pressure: learning-based analog gauge reading in the wild
This addresses the challenge of automated gauge reading for robotics applications, though it appears incremental as it builds on existing methods with specific improvements.
The authors tackled the problem of reading analog gauges in real-world robotic systems by proposing an interpretable framework that splits the task into steps for failure detection, achieving a relative reading error of less than 2%.
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%.