Realism-Oriented Design, Verification, and Validation of Novel Robust Navigation Solutions

Abstract

Urban environments are characterized by a set of conditions underpinning degradation Position, Navigation and Timing (PNT) signals, such as multipath and non-line of sight (NLOS) effects, negatively affecting the position and the navigation integrity during the Uncrewed Aerial Vehicles (UAVs) operations. Before the deployment of such uncrewed aerial platforms, a realistic simulation set-up is required, which should facilitate the identification and mitigation of the performance degradation that may appear during the actual mission. This paper presents the case study of the development of a robust Artificial Intelligence (AI)-based multi-sensor fusion framework using a federated architecture. The dataset for this development, comprising the outputs of a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and a monocular camera is generated in a high-fidelity simulation framework. The simulation framework is built around Spirent’s GSS7000 simulator, software tools from Spirent (SimGEN and SimSENSOR) and OKTAL-SE (Sim3D), where the realism for the vision sensor data generation is provided by a photorealistic environment generated using the AirSim software with the Unreal Engine aid. To verify and validate the fusion framework a hardware in the loop (HIL) set-up has been implemented using the Pixhawk controller. The results obtained demonstrate that the presented HIL set-up is the essential component of a more robust navigation solution development framework, providing resilience under conditions of GNSS outages.

Keywords

• realistic simulation
• sensor fusion
• machine learning
• navigation integrity