Under pressure: learning-based analog gauge reading in the wild

Autor:	Reitsma, Maurits, Keller, Julian, Blomqvist, Kenneth, Siegwart, Roland
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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%. Comment: 7 pages, 8 figures, accepted for presentation at the 2024 IEEE International Conference on Robotics and Automation (ICRA) and for inclusion in the conference proceedings, finalist for the IEEE ICRA 2024 Best Paper Award in Automation, source code https://github.com/ethz-asl/analog_gauge_reader, Autonomous Systems Lab, ETH Zurich
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.08785 Zobrazit plný text záznamu View this record from Arxiv