Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network

Mohammad O. Khan, Gary B. Parker

2019

Abstract

A robust obstacle avoidance control program was developed for a mobile robot in the context of tight, dynamic indoor environments. Deep Learning was applied in order to produce a refined classifier for decision making. The network was trained on low quality raw RGB images. A fine-tuning approach was taken in order to leverage pre-learned parameters from another network and to speed up learning time. The robot successfully learned to avoid obstacles as it drove autonomously in a tight classroom/laboratory setting.

Paper Citation

in Harvard Style

Khan M. and Parker G. (2019). Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA; ISBN 978-989-758-384-1, SciTePress, pages 403-411. DOI: 10.5220/0008165104030411

in Bibtex Style

@conference{ncta19,
author={Mohammad O. Khan and Gary B. Parker},
title={Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA},
year={2019},
pages={403-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008165104030411},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA
TI - Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network
SN - 978-989-758-384-1
AU - Khan M.
AU - Parker G.
PY - 2019
SP - 403
EP - 411
DO - 10.5220/0008165104030411
PB - SciTePress