MaRS: A Modular and Robust Sensor-Fusio...

State-of-the-art recursive sensor filtering frameworks allow the fusion of multiple sensors tailored to a specific problem but do not allow a dynamic and efficient introduction of additional sensors during runtime - an important feature to enable long-term missions in dynamic environments. This paper presents a robust, modular sensor-fusion framework that enables the addition and removal of sensors at runtime. These sensors could be not a priori known to the system. The framework handles the complexity of system and sensor initialization, measurement updates, and switching of asynchronous multi-rate sensor information with sensor self-calibration in a truly modular and generic design. In addition, the framework can handle delayed measurements, out-of-sequence updates, and can monitor sensor health. The introduced true-modularity is based on covariance segmentation to allow the isolated (i.e., modular) processing of propagation and updates on a per-sensor basis. We show how crucial properties of the overall state covariance can be maintained as naive implementation of such a modularization would invalidate the covariance matrix. We evaluate our framework for a precision landing scenario switching between combinations of GNSS, barometer, and vision measurements. Tests are performed in simulation and in real-world scenarios to show the validity of the introduced method. The presented framework will be open-sourced and made available online to the community.