Depth-guided Free-space Segmentation for a Mobile Robot
Christos Sevastopoulos Joey Hussain Stasinos Konstantopoulos Vangelis Karkaletsis Fillia Makedon
Accurate indoor free-space segmentation is a challenging task due to the
complexity and the dynamic nature that indoor environments exhibit. We propose
an indoors free-space segmentation method that associates large depth values
with navigable regions. Our method leverages an unsupervised masking technique
that, using positive instances, generates segmentation labels based on textural
homogeneity and depth uniformity. Moreover, we generate superpixels
corresponding to areas of higher depth and align them with features extracted
from a Dense Prediction Transformer (DPT). Using the estimated free-space masks
and the DPT feature representation, a SegFormer model is fine-tuned on our
custom-collected indoor dataset. Our experiments demonstrate sufficient
performance in intricate scenarios characterized by cluttered obstacles and
challenging identification of free space.