Autonomous robots require robust perception and navigation in adverse environments where sensors like LiDAR, RADAR, and RGB-D cameras often fail or degrade. We propose MOUSE, a novel adaptive multimodal fusion framework that learns the complementary strengths of each sensor and dynamically selects optimal configurations for changing environments. Unlike existing methods, MOUSE jointly learns multiple downstream perception tasks within a unified framework to enhance situational awareness. Furthermore, MOUSE leverages learned semantic information to enhance measurement associations and object classification, enabling enhanced tracking, localization, and navigation in challenging and dynamic environmental conditions.

The project is funded by ELLIIT and led by Eren Aksoy as Principal Investigator, in collaboration with Gustaf Hendeby at Linköping University.
At Lund University, the project includes the doctoral research of Enes Erdogan, supervised by Eren Aksoy and Davide Tateo.