Semantic Segmentation in Autonomous Mobile Robots based on CNN with Fast Attention

Abstract

This paper presents the development of a convolutional neural network architecture for semantic segmentation in autonomous mobile robots. The model comprises a mix of traditional CNNs and transformer-based attention modules. ResNet18 and ResNet34 are employed as backbone models with fast attention modules integrated at the end of each ResNet level. The SUNRGBD dataset is used for training and evaluation, while the final network is tested on a real mobile robot TIAGo. An extensive parameter-tuning procedure is conducted to determine optimal parameters and assess the model's sensitivity to changes in parameters. The proposed approach aims to improve segmentation performance while meeting real-time computational constraints.