A fault tolerant multi-sensor fusion navigation system for drone in urban environment

Precise positioning becomes an attractive research area to enhance last-mile delivery with drones. However, the reliability of precise poisoning is significantly degraded in GNSS-denied environments such as urban canyons. In this case, the excellent performance of Visual Inertial Odometry (VIO) in local pose estimation makes visual navigation technology more feasible for researchers. However, the accuracy and robustness of VIO degrade in faulted conditions. This paper presents a fault-tolerant multisensor fusion navigation system for drones in urban environments. We first performed Failure Mode and Effect Analysis (FMEA) in the VIO system to identify potential failure mode, which is feature extraction errors. Then, an integrated, loosely coupled EKF-based VIO system is proposed for our GNSS/VINS/LIO reference system to mitigate visual and IMU faults. The performance of the proposed method was validated by a synthetic dataset created using MATLAB, and it has shown improved robustness over Visual odometry and state-of-art VINS systems

Failure mode analysis (FMA) for visual-based navigation for UAVs in urban environment

Visual-based navigation systems for Unmanned Aerial vehicles (UAVs) have become an interesting research area focused on improving robustness and accuracy in the urban environment. However, a lack of integrity can damage UAVs, limiting their potential usage in civil applications. For safety reasons, integrity performance requirements must be met. In literature, such systems require significant attention for their ability to perform fault analysis, referred to as failure mode. In this paper, we have conducted a failure mode analysis in urban environments for UAVs to identify threats and faults presented in existing Visual-inertial Navigation Systems. In addition, we propose a federated-filter-based fault detection and execution system to improve navigation performance under faulted conditions

Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments

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