Multiplicative Extended Kalman Filter for Relative Rotorcraft Navigation

In this article we detail the fundamentals of a new approach to GPS-denied navigation for aerial vehicles in confined indoor environments. We depart from the common practice of navigating within a globally referenced map, and instead keep the position and yaw states relative to the current node in the map. The approach combines elements of graph SLAM with a multiplicative extended Kalman filter (MEKF). The filter provides quality state estimates at a fast rate and a graph SLAM algorithm maintains a pose graph. We provide specific details for the relative MEKF. We verify the relative estimation approach with hardware flight test results accompanied by comparisons to motion capture truth. We also provide flight results with estimates in the control loop.