  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. Code, pretrained models and dataset
are available at http://adaptivefusion.cs.uni-freiburg.de

Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning