Decentralized Collaborative State Estimation for Aided Inertial Navigation

This paper introduces a new approach to filtering data from sensors on multiple autonomous agents, such as robots or drones, to estimate the agents’ positions and orientations relative to each other. The proposed method, called Quaternion-based Error-State Extended Kalman Filter (Q-ESEKF), uses advanced mathematical techniques to make the filtering more accurate and efficient. It also incorporates Collaborative State Estimation (CSE), which allows each agent to estimate its own state independently while taking into account measurements from other agents. The paper presents simulation results demonstrating the effectiveness of the approach on two benchmark datasets in 3D.