Multiplicative Extended Kalman Filter for Relative Rotorcraft Navigation

Abstract

This paper details the fundamentals of a new approach to navigation for aerial vehicles in confined indoor environments without access to global-position measurements. The approach departs from the common practice of navigating within a globally referenced map, and it instead keeps the position and yaw states relative to the current node in the map. The approach combines elements of graph-based simultaneous localization and mapping with a multiplicative extended Kalman filter. The filter provides accurate state estimates at a fast rate and provides the information necessary for a simultaneous localization and mapping algorithm to maintain a pose graph. Specific details for the relative multiplicative extended Kalman filter are provided. The relative estimation approach is validated with hardware flight tests, and results are compared to motion capture ground truth data. In addition, flight-test results using estimates in the control loop are provided.