Liquids and granular media are pervasive
throughout human environments. Their free-flowing nature
causes people to constrain them into containers. We do so
with thousands of different types of containers made out
of different materials with varying sizes, shapes, and colors.
In this work, we present a state-of-the-art sensing technique
for robots to perceive what liquid is inside of an unknown
container. We do so by integrating Visible to Near Infrared
(VNIR) reflectance spectroscopy into a robot’s end effector.
We introduce a hierarchical model for inferring the material
classes of both containers and internal contents given spectral
measurements from two integrated spectrometers. To train
these inference models, we capture and open source a dataset
of spectral measurements from over 180 different combinations
of containers and liquids. Our technique demonstrates over
85% accuracy in identifying 13 different liquids and granular
media contained within 13 different containers. The sensitivity
of our spectral readings allow our model to also identify the
material composition of the containers themselves with 96%
accuracy. Overall, VNIR spectroscopy presents a promising
method to give household robots a general-purpose ability to
infer the liquids inside of containers, without needing to open
or manipulate the containers.
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CNN-Based Non-contact Detection of Food Level in Bottles from RGB Images
In this paper, we present an approach that detects the level of food in store-bought containers using deep convolutional neural networks (CNNs) trained on RGB images captured using an off-the-shelf camera. Our approach addresses three challenges—the diversity in container geometry, the large variations in shapes and appearances of labels on store-bought containers, and the variability in color of container contents—by augmenting the data used to train the CNNs using printed labels with synthetic textures attached to the training bottles, interchanging the contents of the bottles of the training containers, and randomly altering the intensities of blocks of pixels in the labels and at the bottle borders. Our approach provides an average level detection accuracy of 92.4% using leave-one-out cross-validation on 10 store-bought bottles of varying geometries, label appearances, label shapes, and content colors.
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- Award ID(s):
- 1730183
- NSF-PAR ID:
- 10094310
- Date Published:
- Journal Name:
- International Conference on Multimedia Modeling
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-05710-7_17
