A software architecture for multi-modal semantic perception fusion

Robots need advanced perceptive systems to interact with the environment and with humans. Integration of different perception modalities increases the system reliability and provides a richer environmental representation. The article proposes a general-purpose architecture to fuse semantic information, extracted by difference perceptive modules. Therefore, the article describes a mockup implementation of our general-purpose architecture to fuse geometric features, computed from point clouds, and Convolution Neural Network (CNN) classifications, based on images.