Robust cooperative visual localization with experimental validation for unmanned aerial vehicles

Abstract

This article aims to present an adaptive and robust cooperative visual localization solution based on stereo vision systems. With the proposed solution, a group of unmanned vehicles, either aerial or ground will be able to construct a large reliable map and localize themselves precisely in this map without any user intervention. For this cooperative localization and mapping problem, a robust nonlinear H∞ filter is adapted to ensure robust pose estimation. In addition, a robust approach for feature extraction and matching based on an adaptive scale invariant feature transform stereo constrained algorithm is implemented to build a large consistent map. Finally, a validation of the solution proposed is presented and discussed using simulation and experimental data.