  Localization is a critical capability for robots, drones and autonomous
vehicles operating in a wide range of environments. One of the critical
considerations for designing, training or calibrating visual localization
systems is the coverage of the visual sensors equipped on the platforms. In an
aerial context for example, the altitude of the platform and camera field of
view plays a critical role in how much of the environment a downward facing
camera can perceive at any one time. Furthermore, in other applications, such
as on roads or in indoor environments, additional factors such as camera
resolution and sensor placement altitude can also affect this coverage. The
sensor coverage and the subsequent processing of its data also has significant
computational implications. In this paper we present for the first time a set
of methods for automatically determining the trade-off between coverage and
visual localization performance, enabling the identification of the minimum
visual sensor coverage required to obtain optimal localization performance with
minimal compute. We develop a localization performance indicator based on the
overlapping coefficient, and demonstrate its predictive power for localization
performance with a certain sensor coverage. We evaluate our method on several
challenging real-world datasets from aerial and ground-based domains, and
demonstrate that our method is able to automatically optimize for coverage
using a small amount of calibration data. We hope these results will assist in
the design of localization systems for future autonomous robot, vehicle and
flying systems.
