186 research outputs found
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
Deep learning is known to be data-hungry, which hinders its application in
many areas of science when datasets are small. Here, we propose to use transfer
learning methods to migrate knowledge between different physical scenarios and
significantly improve the prediction accuracy of artificial neural networks
trained on a small dataset. This method can help reduce the demand for
expensive data by making use of additional inexpensive data. First, we
demonstrate that in predicting the transmission from multilayer photonic film,
the relative error rate is reduced by 46.8% (26.5%) when the source data comes
from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer)
films. Second, we show that the relative error rate is decreased by 22% when
knowledge is transferred between two very different physical scenarios:
transmission from multilayer films and scattering from multilayer
nanoparticles. Finally, we propose a multi-task learning method to improve the
performance of different physical scenarios simultaneously in which each task
only has a small dataset
Exploration with Global Consistency Using Real-Time Re-integration and Active Loop Closure
Despite recent progress of robotic exploration, most methods assume that
drift-free localization is available, which is problematic in reality and
causes severe distortion of the reconstructed map. In this work, we present a
systematic exploration mapping and planning framework that deals with drifted
localization, allowing efficient and globally consistent reconstruction. A
real-time re-integration-based mapping approach along with a frame pruning
mechanism is proposed, which rectifies map distortion effectively when drifted
localization is corrected upon detecting loop-closure. Besides, an exploration
planning method considering historical viewpoints is presented to enable active
loop closing, which promotes a higher opportunity to correct localization
errors and further improves the mapping quality. We evaluate both the mapping
and planning methods as well as the entire system comprehensively in simulation
and real-world experiments, showing their effectiveness in practice. The
implementation of the proposed method will be made open-source for the benefit
of the robotics community
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