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+/**
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+@page dev-roadmap Future Roadmap
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+The initial release provides the library foundation which contains a filter-based visual-inertial localization solution.
+This can be used in a wide range of scenarios and the type-based index system allows for others to easily add new features and develop on top of this system.
+Here is a list of future directions, although nothing is set in stone, that we are interested in taking:
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+- Creation of a secondary graph-based thread that loosely introduces loop closures (akin to the second thread of VINS-Mono and others) which should allow for drift free long-term localization.
+- Large scale offline batch graph optimization which leverages the trajectory of the ov_msckf as its initial guess and then optimizes both the map and trajectory.
+- Incorporate our prior work in preintegration @cite Eckenhoff2019IJRR into the same framework structure to allow for easy extensibility. Focus on sparsification and marginalization to allow for realtime computation.
+- Leverage this sliding-window batch method to perform SFM initialization of all methods.
+- Support for arbitrary Schmidt'ing of state elements allowing for modeling of their prior uncertainties but without optimizing their values online.
+- More advance imu integration schemes, quantification of the integration noises to handle low frequency readings, and modeling of the imu intrinsics.
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+*/