  Modern visual-inertial navigation systems (VINS) are faced with a critical
challenge in real-world deployment: they need to operate reliably and robustly
in highly dynamic environments. Current best solutions merely filter dynamic
objects as outliers based on the semantics of the object category. Such an
approach does not scale as it requires semantic classifiers to encompass all
possibly-moving object classes; this is hard to define, let alone deploy. On
the other hand, many real-world environments exhibit strong structural
regularities in the form of planes such as walls and ground surfaces, which are
also crucially static. We present RP-VIO, a monocular visual-inertial odometry
system that leverages the simple geometry of these planes for improved
robustness and accuracy in challenging dynamic environments. Since existing
datasets have a limited number of dynamic elements, we also present a
highly-dynamic, photorealistic synthetic dataset for a more effective
evaluation of the capabilities of modern VINS systems. We evaluate our approach
on this dataset, and three diverse sequences from standard datasets including
two real-world dynamic sequences and show a significant improvement in
robustness and accuracy over a state-of-the-art monocular visual-inertial
odometry system. We also show in simulation an improvement over a simple
dynamic-features masking approach. Our code and dataset are publicly available.
