Scalable Recursive Distributed Collaborative State Estimation for Aided Inertial Navigation

摘要

This paper presents a novel approach to recover outdated cross-covariance between correlated agents at the moment they perform joint observations. This allows to render Collaborative State Estimation (CSE) fully distributed, with communication only required for the moment of joint observation and most importantly, it significantly reduces the maintenance effort in case of high frequent propagation sensors. These properties make the approach suitable to a wide range of multi-robot applications. In our evaluation on a Quaternion-based Error-State Extended Kalman Filter (Q-ESEKF) using an Inertial Measurement Unit (IMU) as propagation sensor at a rate of 200Hz, we showed a significant speedup against our previous approach for maintaining a couple of interdependence. We compared the approach in total against four different approaches on both, a simulation and on a real-world dataset for Micro Aerial Vehicles (MAVs). Video: https://youtu.be/xkljfwbhMP0