Affiliation:
1. School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Abstract
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.
Reference71 articles.
1. Arafat, M.Y., Alam, M.M., and Moh, S. (2023). Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges. Drones, 7.
2. State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems;Alkendi;IEEE Access,2021
3. Afia, A.B., Escher, A., and Macabiau, C. (2015, January 14–18). A Low-cost GNSS/IMU/Visual monoSLAM/WSS Integration Based on Federated Kalman Filtering for Navigation in Urban Environments. Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, FL, USA.
4. A tightly-coupled compressed-state constraint Kalman Filter for integrated visual-inertial-Global Navigation Satellite System navigation in GNSS-Degraded environments;Lee;IET Radar Sonar Navig.,2022
5. Enhancing navigation performance through visual-inertial odometry in GNSS-degraded environment;Liao;GPS Solut.,2021