A Deep Learning Approach for Robust Corridor Following

TL;DR

This paper introduces an end-to-end convolutional neural network approach for robust autonomous corridor following, effectively handling environmental noise and absent features, demonstrated on a wheelchair platform.

Contribution

The work presents a CNN-based method that integrates feature extraction and control law computation for corridor following, improving reliability over traditional techniques.

Findings

CNN outperforms traditional methods in noisy environments

End-to-end approach simplifies corridor following control

Validated on a custom wheelchair platform

Abstract

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.