Resilient Sensor Architecture Design and Tradespace Analysis for Autonomous Vehicle Localization and Mapping
This addresses the problem of designing cost-effective and resilient sensor networks for autonomous vehicles, but it is incremental as it builds on existing SLAM and sensor analysis methods.
The paper tackles the trade-off between cost, performance, and resiliency in sensor suites for autonomous vehicle SLAM by introducing a method that recommends sensor combinations based on the operating environment, demonstrating it on the KITTI Benchmark Suite.
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.