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.
@cite_11: The list of possible sensors supporting autonomous tasks in a vehicle is considerable. Sensors are one of the main drivers of costs for current prototypes of autonomous vehicles. As production volume increases, companies have to take into account this cost, and consider whether the increase in performance given by the addition of redundancy in sensing is worth the cost. This paper analyzes sensor fusion from a systems perspective, and proposes a method based on factor graphs to quantity the error given by a combination of sensors for the simultaneous localization and mapping task, as a function of the individual sensor capabilities. A total of 81 different combinations are analyzed, with 4 different types of sensors, and varying levels of performance and cost within each type of sensors. This analysis reveals several findings; in some cases, the addition of sensors can decrease the performance of the system by adding noise, and there is a cost over which performance stops increasing. Additionally, we quantity the intuitive idea that systems including sensors working in adverse conditions might still perform well if there are other complementary sensor to provide reliable information to the system.
However, to the knowledge of the authors, there exists a gap within the application space of such methods to help guide and inform the sensor choice and placement on autonomous vehicles, and to aid in the quantification of the tradespace between performance and non-uniform sensor costs. Initial efforts that make use of similar techniques have been presented @cite_11 , but they still lack a systematic strategy to tackle the sensor combination enumeration. Furthermore, none of these advances consider the resiliency of sensor networks.