Indoors Traversability Estimation with Less Labels for Mobile Robots

We present a method for binary (go/no-go) indoors traversability estimation from 2D images. Our method exploits the power of a pre-trained Vision Transformer (ViT) which we fine-tune on our own dataset. We conduct experiments using a mobile robotic platform to gather image data. Our fine-tuning approach includes the use of a pre-trained Vision Transformer (ViT) en route towards developing a semi-supervised deep learning technique to enhance indoor traversability estimation for scenarios where only a small amount of data is available. We evaluate the accuracy and generalization power of our method against well-established state-of-the-art deep architectures for image classification such as ResNet, and show improved performance.