Evaluation of the Benefits of Zero Velocity Update in Decentralized Extended Kalman Filter-Based Cooperative Localization Algorithms for GNSS-Denied Multi-Robot Systems

Abstract

This paper proposes the cooperative use of zero velocity update (ZU) in a decentralized extended Kalman filter (DEKF)-based localization algorithm for multi-robot systems. The filter utilizes inertial measurement unit (IMU), ultra-wideband (UWB), and odometer-based velocity measurements to improve the localization performance of the system in a GNSS-denied environment. In this work, we evaluate the benefits of using ZU in a DEKF-based localization algorithm. The algorithm was tested with real hardware in a video motion capture facility and a robot operating system (ROS)-based simulation environment for unmanned ground vehicles (UGVs). Both simulation and real-world experiments were performed to determine the effectiveness of using ZU in one robot to reinstate the localization of the others in a multi-robot system. Experimental results from GNSS-denied simulation and real-world environments revealed that using ZU in the DEKF together with simple heuristics significantly improved the three-dimensional localization accuracy.