Learning Indoors Free-Space Segmentation for a Mobile Robot from Positive Instances

Abstract

Accurate indoor free-space segmentation is a demanding task due to the intricate and dynamic nature of indoor environments. We propose an indoors free-space segmentation method that associates large depth values with navigable regions. Our method employs an unsupervised masking technique that, using positive instances, generates segmentation labels based on textural homogeneity and depth uniformity. Using the estimated free-space masks and a Dense Prediction Transformer (DPT) feature representation, a SegFormer model is fine-tuned on our custom-collected indoor dataset. Our experiments demonstrate sufficient performance in complex scenes where the identification of free space is challenging.