Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

This paper presents a distributed leader-assistive localization approach for a heterogeneous multirobotic system (MRS). The localization algorithm is formulated to estimate the position and orientation (pose) of a group of robots in a given reference coordinate frame (or global coordinate frame). It is assumed that the heterogeneous-MRS has one or a group of robots (which we refer as leader robots) with higher sensor payload, higher processing power, and larger memory capacity, enabling accurate self-localization capabilities. Robots with limited resources (which we refer as child robots) rely on leader robots, and inter-robot observations between leaders and themselves for localization. Finite-range sensing is a key challenge for such leader-assistive localization. This study presents a sensor sharing technique which virtually enhances the sensing range of leader robots. In the proposed method, each robot locally runs a cubature Kalman filter to estimate its own pose and hosts a low cost, lightweight, and low-power sensory system to periodically measure relative pose of neighbors. Each robot transmits these relative pose measurements to leader robots. Leader robots then combine available relative observations in order to synthesise global pose measurements and associated noise covariances for child robots. Child robots are acknowledged by the leader robots with the synthesized global pose measurements and fuse these measurements with their local belief in order to improve their localization. Theoretical developments are presented to virtually enhance the leader robots´ sensing range. The performance of the proposed distributed leader-assistive localization algorithm is evaluated on a multirobot simulation test-bed and on a publicly available multirobot localization and mapping data-set. The results illustrate that the proposed algorithm is capable of establishing accurate and consistent localization for the child robots even when they operate beyond the sensing r- nge of the leader robots. Note to Practitioners-MRS can be used to perform environmental monitoring, exploration tasks, and search and rescue missions. Accurate localization is a critical factor that governs the success of the autonomous mobile robots-based missions. In order to improve the localization accuracy, robots can be equipped with advanced sensory systems which will increase the cost. Additionally, robots can execute advanced localization algorithms to generate an accurate localization which entails higher processing capabilities and higher memory capacity. Most of the robotic systems do not possess sufficient resources to host advanced sensory systems and execute advanced localization algorithms. In a heterogeneousMRS, robots with more accurate localization capabilities (leader robots) can assist robot with limited resources (child robots) for localization. Available leader-assistive localization approaches demand child robots to operate within the sensing range of the leader robots. This constraint limits the teammates´ maneuverability, reduces the area covered by the robots, and demands a complex algorithm to avoid collisions among teammates. We address this limitation and propose a novel leader-assistive localization framework. The proposed framework is capable of establishing an accurate and consistent pose estimation for child robots even when they operate beyond the sensing range of leader robots.