Probabilistic sensor data processing for robot localization on load-sensing floors

Abstract : Load-sensing floors are capable of tracking objects without suffering from occlusions nor posing the same privacy issues as cameras. They have been mostly used to analyze human gait as a way of continuous diagnosis but could also be placed alongside robots to help monitoring in specialized institutions, such as elderly care facilities. However, large-scale deployments necessitate cheap sensors which do not necessarily offer the same precision. With more noisy sensors, lighter robots might be difficult to track and precisely localize. In this article, we investigate various models in order to estimate the position of a robot. We experiment with several robots of different weights and compare the models' estimates against ground truth measurements provided by a motion capture system. We show that with standard-sized tiles of 60 cm, we can track even the lighter robots with less than 4 cm of error.
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Maxime Rio, Francis Colas, Mihai Andries, François Charpillet. Probabilistic sensor data processing for robot localization on load-sensing floors. IEEE International Conference on Robotics and Automation (ICRA), May 2016, Stockholm, Sweden. ⟨hal-01274696⟩

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