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Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments
Oier Mees, Andreas Eitel, Wolfram Burgard IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016
mees16iros.pdf
Notes: Object detection is an essential task for autonomous
robots operating in dynamic and changing environments.
A robot should be able to detect objects in the presence
of sensor noise that can be induced by changing lighting
conditions for cameras and false depth readings for range
sensors, especially RGB-D cameras. To tackle these challenges,
we propose a novel adaptive fusion approach for object detection
that learns weighting the predictions of different sensor
modalities in an online manner. Our approach is based on a
mixture of convolutional neural network (CNN) experts and
incorporates multiple modalities including appearance, depth
and motion. We test our method in extensive robot experiments,
in which we detect people in a combined indoor and outdoor
scenario from RGB-D data, and we demonstrate that our
method can adapt to harsh lighting changes and severe camera
motion blur. Furthermore, we present a new RGB-D dataset
for people detection in mixed in- and outdoor environments,
recorded with a mobile robot.
BibTeX:
@inproceedings{mees16iros,
author = {Oier Mees and Andreas Eitel and Wolfram Burgard},
title = {Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = 2016,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/mees16iros.pdf},
address = {Daejeon, Korea}
}
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