In this paper we present BOEM-SLAM, a backend for visual-inertial SLAM systems capable of creating a globally consistent trajectory and map without retaining the entire history of data. By leveraging the hidden Markov model structure, BOEM-SLAM can summarize historical data into sufficient statistics and then discard it. As a data-efficient algorithm, BOEM-SLAM addresses the growing computational costs and storage requirements of the SLAM backend. To demonstrate the performance of our algorithm we compare BOEM-SLAM to other fundamental approaches on both synthetic data and the EuRoC dataset. For evaluation on the EuRoC dataset, we use the open source okvis frontend and apply the Lie group state space representation and visual outlier removal. Overall, BOEM-SLAM shows a considerably lower computation time with comparable estimation performance. For example, the processing time of BOEM-SLAM is 50 times smaller than the optimization-based method using simulated data and 5 times smaller than the optimization-based method in the EuRoC dataset experiments.

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple interrobot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when interrobot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.

Random access has been widely studied in literature, but its time dynamics remains a pretty open research problem at this time, particularly for cognitive radio networks that are operating most in transient status but being investigated usually in steady-state. Modifying prey-predator model, we therefore consider radio resources as preys and users as predators to dynamically understand the network system behavior. We start from exploring ALOHA, then include the sensing mechanism into the scenario. Furthermore, we incorporate partially or randomly connected graph to practically represent realistic interactions among users and resources. By modeling sensing errors and delay, for the first time, the time dynamics of a cognitive radio network can be fully characterized, and consequently random access operating conditions can be practically understood and specified for network engineering design.