Scene Segmentation is an important component for robots which are required to navigate in an indoor environment. Obstacle avoidance is the task of detecting and avoiding obstacles and represents a hot topic for autonomous robots. To obtain a collision free motion, a robust module for obstacle detection is needed. The objective of this thesis is to make a robot able to navigate autonomously, relying only on visual perception, performing real-time segmentation of the indoor scene. In accordance with the state of the art, the proposed method is based on a Deep Learning model for Semantic Scene Segmentation. A Pyramid Scene Parsing (PSP) Net with a ResNet-34 as a backbone is chosen as a model to train.