Browsing by Author "Tabassum, Tarafder Elmi"

Now showing 1 - 4 of 4
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    Failure mode analysis (FMA) for visual-based navigation for UAVs in urban environment
    (UK-Robotics and Autonomous Systems (UK-RAS) Network, 2022-08-26) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan
    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.
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    A fault tolerant multi-sensor fusion navigation system for drone in urban environment
    (German Institute of Navigation, 2022-11-04) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan
    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.
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    Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments
    (MDPI, 2023-10-29) Tabassum, Tarafder Elmi; Xu, Zhengjia; Petrunin, Ivan; Rana, Zeeshan A.
    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.
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    Position uncertainty reduction in visualInertial navigation systems using multi-ML error compensation
    (Institute of Navigation, 2024-10-09) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan A
    In the absence of signals from global navigation satellite systems (GNSS), visual-inertial navigation systems (VINS) are usually utilized in urban air mobility (UAM) applications which require reliable navigation in complex urban areas. This paper focuses on improving vision-based alternatives to GNSS navigation solutions with position uncertainty correction for safe and uninterrupted flights. The novel contribution introduces multiple machine-learning aided hybrid visual-inertial odometry (multi-ML hybrid VIO) that utilizes gated recurrent unit (GRU) based error compensators to enhance positioning within complex environments by reducing the impacts of various sources of uncertainty. Unlike state-of-the-art systems that lack evidence of demonstrating performance enhancement with position uncertainty correction within VIO architectures, the proposed framework simultaneously reduces position uncertainty and improves accuracy. Furthermore, training and testing datasets are generated using MATLAB incorporating unreal engine simulation environment for UAVs to replicate complex scenarios including environmental conditions, illumination variations, weather effects and flight dynamics where traditional VIO systems tend to fail. The proposed hybrid VIO architecture has been validated under combinations of complex scenarios including various sources of uncertainty such as sensor noise, feature tracking error, environmental dynamics, weather effects and lighting conditions for extended flights. The comparison results have demonstrated reduction in horizontal positioning RMSE errors: 1.7m for VIO with VO error compensation 2.18m for VIO with KF error compensation, 1.4m for multi-ML hybrid VIO. Furthermore, it demonstrates generalization ability over seen and unseen fault scenarios that indicates performance improvement of 89% in 3D position compared to VIO with VO error compensation, VIO with KF error compensation, and ESKF-based VIO. Additionally, experimental results demonstrate overall horizontal position uncertainty reduction by 79% for test 1 and 22% for test 2. Finally, this work represents a step forward in improving the safety and effectiveness of UAV navigation by providing vision-based alternative to GNSS solution for uninterrupted flights.