Situational Awareness Strategies for Autonomous Systems in Dynamic Uncertain Environments

Abstract: An event-based communication strategy for navigation using signals of opportunity (SOPs) in a collaborative radio simultaneous and mapping (CoRSLAM) framework is developed. The following problem is considered. Multiple autonomous vehicles (AVs) with access to global navigation satellite system (GNSS) signals are aiding their on-board inertial navigation systems (INSs) with GNSS pseudoranges. While navigating, AV-mounted receivers draw pseudorange measurements on ambient unknown terrestrial SOPs and collaboratively estimate the SOPs' states. After some time, GNSS signals become unavailable, at which point the AVs use the SOPs to aid their INSs in a CoRSLAM framework. An event-based transmission strategy to share time-of-arrival (TOA) measurements from SOPs is developed, which is shown to significantly reduce the required communication rate compared to a fixed-rate scheme, while maintaining a specified maximum positioning error with a desired probability. Experimental results are presented demonstrating unmanned aerial vehicles (UAVs) navigating with the CoRSLAM framework, reducing the final localization error after 30 seconds of GPS unavailability from around 55 m to around 6 m.

Explanation of Demonstration: The demo will showcase lane-level-accurate ground vehicle navigation by fusing Lidar with cellular long-term evolution (LTE) signals, without GPS signals. The vehicle simultaneously estimates its own pose (three-dimensional position and orientation), the LTE towers' three-dimensional position, and the difference between the vehicle's receiver clock bias and drift and the bias and drift of each LTE tower. The objective is to use a minimal number of the lidar point cloud (only 4.6% of the points returned by the lidar) to enable real-time implementations. The framework uses LTE signals in a feedback fashion to correct the errors that accumulate in the lidar-derived estimate, due to using such minimal number of points.