Multiple model based state estimation and trajectory control for micro aerial vehicles

Abstract

This thesis proposes the design of a multiple model state estimation and control scheme for micro aerial vehicles (MAVs) to cope with different flight conditions such as aggressive flights, hovering flights, and flights under high external disturbances. The work is divided into two main parts. The first part of this thesis presents the design of an interacting multiple model (IMM) filter for visual-inertial navigation (VIN) of MAVs. VIN of MAVs in practice typically uses a single system model for its state estimator design. However, MAVs can operate in different scenarios requiring changes to the estimator model. This thesis proposes the use of a conventional VIN and a drag force VIN in an error-state IMM filtering framework to address the need for multiple models in the estimator. We use an epipolar geometry constraint for the design of the measurement model for both filters to realize computationally efficient state updates. Observability of the proposed modifications to VIN filters (drag force model, and epipolar measurement model) are analyzed, and observability-based consistency rules are derived for the two filters of the IMM. Monte Carlo numerical simulations validate the performance of the observability constrained IMM, which improved the accuracy and consistency of the VINS for changing flight conditions and external wind disturbance scenarios. Experimental validation is performed using the EuRoC dataset to evaluate the performance of the proposed IMM filter design.