Multi-sensor ATTenuation Estimation (MATTE): Signal-strength prediction for teams of robots

Multi-robot teams are often constrained by communications; better signal-strength models enable more efficient coordination while still maintaining adequate communication. This work discusses several prediction algorithms applicable to this scenario. Whereas previous approaches typically focus on prediction in the presence of deployed base-stations, we consider the more general problem where all nodes in the network can be mobile. Our new algorithm, Multi-sensor ATTenuation Estimation (MATTE), addresses this problem by leveraging other forms of sensor data in combination with signal-strength measurements to infer the locations of attenuating materials in the robots´ environment. We also extend prior tomographic and correlation-based approaches to the multi-robot case, allowing a competitive evaluation. All methods are evaluated on a large corpus of real-world indoor and outdoor environments.