Deep learning based semantic segmentation of unstructured outdoor environments

Abstract

The past decade has seen increased interest in, and demand for autonomous vehicles. Complete and successful autonomy of mobile systems such as unmanned ground vehicles (UGVs) depends on perception. Perception is the ability of an agent to semantically interpret its operational environment through vision. Deep learning approaches have recently been continuously more successful over traditional/classical methods in perception and vision tasks. This is primarily due to their non-reliance on selected hand-crafted features, they adopt a more robust and generalised learned-feature approach through representation learning. Convolutional Neural Networks (CNNs) have been widely used towards the goal of scene parsing and perception. In this research we focus on using CNN architectures for semantic segmentation in unstructured outdoor environments for autonomous navigation. Our first contribution is to provide a novel dataset for unstructured outdoor domains: the CSIR dataset. We seek to establish whether it is possible to semantically segment an unstructured scene into pre-defined classes such as grass, road, sky, trees etc. This is achieved through an exhaustive comparative study on state-of-the-art CNN architectures on this dataset, and a similar additional dataset: the Freiburg Forest dataset. Furthermore, we seek to establish whether there are any benefits in using transfer learning and pre-trained weights in training CNN architectures for semantic segmentation with limited datasets. Lastly, we identify the important architectural factors necessary for successful semantic segmentation in unstructured outdoor scenes.