  External effects such as shocks and temperature variations affect the
calibration of visual-inertial sensor systems and thus they cannot fully rely
on factory calibrations. Re-calibrations performed on short user-collected
datasets might yield poor performance since the observability of certain
parameters is highly dependent on the motion. Additionally, on
resource-constrained systems (e.g mobile phones), full-batch approaches over
longer sessions quickly become prohibitively expensive.
  In this paper, we approach the self-calibration problem by introducing
information theoretic metrics to assess the information content of trajectory
segments, thus allowing to select the most informative parts from a dataset for
calibration purposes. With this approach, we are able to build compact
calibration datasets either: (a) by selecting segments from a long session with
limited exciting motion or (b) from multiple short sessions where a single
sessions does not necessarily excite all modes sufficiently. Real-world
experiments in four different environments show that the proposed method
achieves comparable performance to a batch calibration approach, yet, at a
constant computational complexity which is independent of the duration of the
session.
