Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network

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.