Scalable and Modular Ultra-Wideband Aided Inertial Navigation

Abstract

Navigating accurately in potentially GPS-denied environments is a perquisite of autonomous systems. Relative localization based on ultra-wideband (UWB) is - especially indoors - a promising technology. In this paper, we present a probabilistic filter based Modular Multi-Sensor Fusion (MMSF) approach with the capability of using efficiently all information in a fully meshed UWB ranging network. This allows an accurate mobile agent state estimation and the calibration of the ranging network's spatial constellation. We advocate a new paradigm that includes elements from Collaborative State Estimation (CSE) and allows us considering all stationary UWB anchors and the mobile agent as a decentralized set of estimtors/filters. With this, our method can include all meshed (inter-)sensor observations tightly coupled in a modular estimator. We show that the application of our CSE-inspired method in such a context breaks the computational barrier. Otherwise, it would, for the sakeof complexity-reduction, prohibit the use of all available information or would lead to significant estimator inconsistencies due to coarse approximations. We compare the proposed approach against different MMSF strategies in terms of execution time, accuracy, and filter credibility on both synthetic data and on a dataset from real Unmanned Aerial Vehicles (UAVs).