  As autonomous cars are rolled out into new environments, their ability to
solve the simultaneous localization and mapping (SLAM) problem becomes
critical. In order to tackle this problem, autonomous vehicles rely on sensor
suites that provide them with information about their operating environment.
When large scale production is taken into consideration, a trade-off between an
acceptable sensor suite cost and its resulting performance characteristics
arises. Furthermore, guaranteeing the system's performance requires a resilient
sensor network design. This work seeks to address such trade-offs by
introducing a method that takes into account the performance, cost, and
resiliency of distinct sensor selections. As a result, this method is able to
offer sensor combination recommendations based on the vehicle's operating
environment. It is found that the structure of the environment influences
sensor placement, and that the design of a resilient sensor network involves
careful consideration of both environmental attributes such as landmark density
and location, as well as the available types of complimentary sensors.
Demonstration of the proposed approach is shown by evaluating it using
sequences from the KITTI Benchmark Suite.
