  For an autonomous corridor following task where the environment is
continuously changing, several forms of environmental noise prevent an
automated feature extraction procedure from performing reliably. Moreover, in
cases where pre-defined features are absent from the captured data, a well
defined control signal for performing the servoing task fails to get produced.
In order to overcome these drawbacks, we present in this work, using a
convolutional neural network (CNN) to directly estimate the required control
signal from an image, encompassing feature extraction and control law
computation into one single end-to-end framework. In particular, we study the
task of autonomous corridor following using a CNN and present clear advantages
in cases where a traditional method used for performing the same task fails to
give a reliable outcome. We evaluate the performance of our method on this task
on a Wheelchair Platform developed at our institute for this purpose.

Robotics;Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing