CERES :: Browsing by Author "Aouf, Nabil"

Browsing by Author "Aouf, Nabil"

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Open Access
3D automatic target recognition for future LIDAR missiles
(IEEE, 2017-01-10) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Richardson, Mark A.
We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches.
Open Access
3D automatic target recognition for missile platforms
(2017-05) Kechagias Stamatis, Odysseas; Aouf, Nabil
The quest for military Automatic Target Recognition (ATR) procedures arises from the demand to reduce collateral damage and fratricide. Although missiles with two-dimensional ATR capabilities do exist, the potential of future Light Detection and Ranging (LIDAR) missiles with three-dimensional (3D) ATR abilities shall significantly improve the missile’s effectiveness in complex battlefields. This is because 3D ATR can encode the target’s underlying structure and thus reinforce target recognition. However, the current military grade 3D ATR or military applied computer vision algorithms used for object recognition do not pose optimum solutions in the context of an ATR capable LIDAR based missile, primarily due to the computational and memory (in terms of storage) constraints that missiles impose. Therefore, this research initially introduces a 3D descriptor taxonomy for the Local and the Global descriptor domain, capable of realising the processing cost of each potential option. Through these taxonomies, the optimum missile oriented descriptor per domain is identified that will further pinpoint the research route for this thesis. In terms of 3D descriptors that are suitable for missiles, the contribution of this thesis is a 3D Global based descriptor and four 3D Local based descriptors namely the SURF Projection recognition (SPR), the Histogram of Distances (HoD), the processing efficient variant (HoD-S) and the binary variant B-HoD. These are challenged against current state-of-the-art 3D descriptors on standard commercial datasets, as well as on highly credible simulated air-to-ground missile engagement scenarios that consider various platform parameters and nuisances including simulated scale change and atmospheric disturbances. The results obtained over the different datasets showed an outstanding computational improvement, on average x19 times faster than state-of-the-art techniques in the literature, while maintaining or even improving on some occasions the detection rate to a minimum of 90% and over of correct classified targets.
Open Access
Automatic x-ray image segmentation and clustering for threat detection
(SPIE, 2017-10-05) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Nam, David; Belloni, Carole
Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers’ attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture’s promising performance.
Open Access
Autonomous navigation for mobility scooters: a complete framework based on open-source software
(IEEE, 2019-11-28) Cecotti, Marco; Kanchwala, Husain; Aouf, Nabil
In recent years, there has been a growing demand for small vehicles targeted at users with mobility restrictions and designed to operate on pedestrian areas. The users of these vehicles are generally required to be in control for the entire duration of their journey, but a lot more people could benefit from them if some of the driving tasks could be automated. In this scenario, we set out to develop an autonomous mobility scooter, with the aim to understand the commercial feasibility of a similar product. This paper reports on the progress of this project, proposing a framework for autonomous navigation on pedestrian areas, and focusing in particular on the construction of suitable costmaps. The proposed framework is based on open-source software, including a library created by the authors for the generation of costmaps.
Open Access
B-HoD: A Lightweight and Fast Binary descriptor for 3D Object Recognition and Registration
(IEEE, 2017-08-03) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Chermak, Lounis
3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance.
Open Access
Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space
(Elsevier, 2020-04-07) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.; Dubois-Matra, Olivier
Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG, Fast-Hessian, FAST, CenSurE detectors and the SIFT, SURF, LIOP, ORB, BRISK, FREAK descriptors are benchmarked for images of Envisat. It was found that a combination of Fast-Hessian with BRISK was the most robust, while still capable of running on a low resolution and acquisition rate setup. For large baselines, the rate of false-positives increases, limiting their use in model-based strategies.
Open Access
Biomimetic vision-based collision avoidance system for MAVs.
(2017-05) Isakhani, Hamid; Aouf, Nabil; Whidborne, James F.
This thesis proposes a secondary collision avoidance algorithm for micro aerial vehicles based on luminance-difference processing exhibited by the Lobula Giant Movement Detector (LGMD), a wide-field visual neuron located in the lobula layer of a locust’s nervous system. In particular, we address the design, modulation, hardware implementation, and testing of a computationally simple yet robust collision avoidance algorithm based on the novel concept of quadfurcated luminance-difference processing (QLDP). Micro and Nano class of unmanned robots are the primary target applications of this algorithm, however, it could also be implemented on advanced robots as a fail-safe redundant system. The algorithm proposed in this thesis addresses some of the major detection challenges such as, obstacle proximity, collision threat potentiality, and contrast correction within the robot’s field of view, to establish and generate a precise yet simple collision-free motor control command in real-time. Additionally, it has proven effective in detecting edges independent of background or obstacle colour, size, and contour. To achieve this, the proposed QLDP essentially executes a series of image enhancement and edge detection algorithms to estimate collision threat-level (spike) which further determines if the robot’s field of view must be dissected into four quarters where each quadrant’s response is analysed and interpreted against the others to determine the most secure path. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios in order to validate the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision (real-world), moving at 1.2 msˉ¹ with a successful avoidance rate of 90% processing at an extreme frequency of 120 Hz, that is much superior compared to the results reported in the contemporary related literature to the best of our knowledge.
Open Access
ChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimation
(IEEE, 2022-07-22) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.
This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electro-optical red-green-blue (RGB) inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
Open Access
A comparison of trajectory planning and control frameworks for cooperative autonomous driving
(American Society of Mechanical Engineers, 2021-01-07) Bezerra Viana, Icaro; Kanchwala, Husain; Ahiska, Kenan; Aouf, Nabil
This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single non-linear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the non-linear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behaviour. In the tests and evaluation of the proposed methodologies, MATLAB-CARSIM co-simulation is utilized. CARSIM provides the high fidelity model for the multi-body vehicle dynamics. MATLAB-CARSIM conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles.
Open Access
DeepLO: Multi-projection deep LIDAR odometry for space orbital robotics rendezvous relative navigation
(Elsevier, 2020-07-30) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Dubanchet, Vincent; Richardson, Mark A.
This work proposes a new Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative robotic space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture suggests a Deep Recurrent Convolutional Neural Network (DRCNN) that exploits multi-projected imagery of the acquired 3D LIDAR data. Advantages of the proposed DRCNN are; an effective feature representation facilitated by the Convolutional Neural Network module within DRCNN, a robust modeling of the navigation dynamics due to the Recurrent Neural Network incorporated in the DRCNN, and a low processing time. Our trials evaluate several current state-of-the-art space navigation methods on various simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Additionally, we evaluate real satellite LIDAR data acquired in our lab. Results demonstrate that the proposed architecture, although trained solely on simulated data, is highly adaptable and is more appealing compared to current algorithms on both simulated and real LIDAR data scenarios affording better odometry accuracy at lower computational requirements.
Open Access
Evaluating 3D local descriptors and recursive filtering schemes for LIDAR based uncooperative relative space navigation
(Wiley, 2019-09-05) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Dubanchet, Vincent
We propose a light detection and ranging (LIDAR)‐based relative navigation scheme that is appropriate for uncooperative relative space navigation applications. Our technique combines the encoding power of the three‐dimensional (3D) local descriptors that are matched exploiting a correspondence grouping scheme, with the robust rigid transformation estimation capability of the proposed adaptive recursive filtering techniques. Trials evaluate several current state‐of‐the‐art 3D local descriptors and recursive filtering techniques on a number of both real and simulated scenarios that involve various space objects including satellites and asteroids. Results demonstrate that the proposed architecture affords a 50% odometry accuracy improvement over current solutions, while also affording a low computational burden. From our trials we conclude that the 3D descriptor histogram of distances short (HoD‐S) combined with the adaptive αβ filtering poses the most appealing combination for the majority of the scenarios evaluated, as it combines high quality odometry with a low processing burden.
Open Access
Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities
(Taylor and Francis, 2017-08-14) Kechagias-Stamatis, Odysseas; Aouf, Nabil
Future light detection and ranging seeker missiles incorporating 3D automatic target recognition (ATR) capabilities can improve the missile’s effectiveness in complex battlefield environments. Considering the progress of local 3D descriptors in the computer vision domain, this paper evaluates a number of these on highly credible simulated air-to-ground missile engagement scenarios. The latter take into account numerous parameters that have not been investigated yet by the literature including variable missile – target range, 6-degrees-of-freedom missile motion and atmospheric disturbances. Additionally, the evaluation process utilizes our suggested 3D ATR architecture that compared to current pipelines involves more post-processing layers aiming at further enhancing 3D ATR performance. Our trials reveal that computer vision algorithms are appealing for missile-oriented 3D ATR.
Open Access
Explainability of deep SAR ATR through feature analysis
(IEEE, 2020-10-20) Belloni, Carole; Aouf, Nabil; Balleri, Alessio; Le Caillec, Jean-Marc; Merlet, Thomas
Understanding the decision-making process of deep learning networks is a key challenge which has rarely been investigated for Synthetic Aperture Radar (SAR) images. In this paper, a set of new analytical tools is proposed and applied to a Convolutional Neural Network (CNN) handling Automatic Target Recognition (ATR) on two SAR datasets containing military targets.
Open Access
FPGA-based multi-sensor relative navigation in space: Preliminary analysis in the framework of the I3DS H2020 project
(Internation Astronautical Federation, 2018-10-04) Estébanez Camarena, Monica; Feetham, Luke; Scannapieco, Antonio; Aouf, Nabil
The Horizon 2020 Integrated 3D Sensors (I3DS) project brings together the following entities throughout Europe: THALES ALENIA SPACE - France / Italy / UK / Spain, SINTEF (Norway), TERMA (Denmark), COSINE (Netherlands), PIAP Space (Poland), HERTZ Systems (Poland), and Cranfield University (UK). I3DS is co-funded under the Horizon 2020 EU research and development program and is part of the Strategic Research Cluster on Space Robotics Technologies. The ambition of I3DS is to produce a standardised modular Inspector Sensor Suite (INSES) for autonomous orbital and planetary applications for future space missions. Orbital applications encompass activities such as on-orbit servicing and repair, space rendezvous and docking, collision avoidance and active debris removal (ADR). Simultaneous localisation and surface mapping (SLAM) for planetary exploration and general navigation in an unknown environment for scientific purposes can be considered in planetary applications. These envisaged space applications can be tackled by exploiting the flexibility, high performance and long product life of FPGAs. Conventional FPGAs are subject to Single Event Upsets (SEU) due to space radiation, causing their failure. Therefore, space-graded FPGAs, such as those developed by Xilinx, are targeted within the I3DS project. Currently, the main use of the FPGA within the development of this robust end-to-end multi-sensor suite is for navigation and data pre-processing. The aim of this paper is to assess the capabilities of FPGAs to carry out complex operations, such as running navigation algorithms for space applications. The motivation for the development of the on-board software architecture is as follows: raw data, acquired from the various sensors – including, among others, a High Resolution camera, a stereo camera and a LiDAR – is pre-processed to ensure the provision of robust and optimised inputs to 3D navigation algorithms. Noise reduction and conversion into suitable formats for the successful application of navigation algorithms are therefore the main aims of the data pre-processing. Some techniques adopted in this phase include outlier rejection and data dimensionality reduction for large point clouds, e.g. from LiDAR, and geometric and radiometric correction of the images from the cameras. The pre-processed data will then feed state-of-the-art relative navigation algorithms. Some of the proposed navigation algorithms include Generalised Iterative Closest Point (GICP) for dense 3D point clouds, relative positioning with fiducial markers, and visual odometry. The system environment for the preliminary operation is a test-bench setup formed by a standard desktop computer and a non-space-graded FPGA (Xilinx UltraZed-EG FPGA). The choice of FPGA was based on the similarity of this board to other space-graded ones also provided by Xilinx. Experimental tests on the algorithms are being performed in the framework of the validation campaign for the I3DS project. Preliminary results indicate that the data pre-processing can be efficiently carried out on the FPGA board.
Open Access
A furcated visual collision avoidance system for an autonomous micro robot
(IEEE, 2018-07-23) Isakhani, Hamid; Aouf, Nabil; Kechagias-Stamatis, Odysseas; Whidborne, James F.
This paper proposes a secondary reactive collision avoidance system for micro class of robots based on a novel approach known as the Furcated Luminance-Difference Processing (FLDP) inspired by the Lobula Giant Movement Detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges; obstacle proximity & direction estimation, and operation in GPS-denied environment with irregular lighting. Additionally, it has proven effective in detecting edges independent of background color, size, and contour. The FLDP executes a series of image enhancement and edge detection algorithms to estimate collision threat-level which further determines whether or not the robot’s field of view must be dissected where each section’s response is compared against the others to generate a simple collision-free maneuver. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios validating the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision, moving at 1.2 ms¯¹ with a successful avoidance rate of 90% processing at 120 Hz on a simple single core microcontroller, sufficient to conclude the system’s feasibility for real-time real-world applications that possess fail-safe collision avoidance system.
Open Access
Fusing deep learning and sparse coding for SAR ATR
(IEEE, 2018-08-10) Kechagias-Stamatis, Odysseas; Aouf, Nabil
We propose a multi-modal and multi-discipline data fusion strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar imagery. Our architecture fuses a proposed Clustered version of the AlexNet Convolutional Neural Network with Sparse Coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet which is 99.33% and 99.86% for the 3 and 10-class problems respectively.
Open Access
High-speed multi-dimensional relative navigation for uncooperative space objects
(Elsevier, 2019-05-03) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Richardson, Mark A.
This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation.
Open Access
Histogram of distances for local surface description
(IEEE, 2016-06-09) Kechagias-Stamatis, Odysseas; Aouf, Nabil
3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance.
Open Access
H∞ LIDAR odometry for spacecraft relative navigation
(IET, 2016-01-04) Kechagias-Stamatis, Odysseas; Aouf, Nabil
Current light detection and ranging (LIDAR) based odometry solutions that are used for spacecraft relative navigation suffer from quite a few deficiencies. These include an off-line training requirement and relying on the iterative closest point (ICP) that does not guarantee a globally optimum solution. To encounter this, the authors suggest a robust architecture that overcomes the problems of current proposals by combining the concepts of 3D local feature matching with an adaptive variant of the H∞ recursive filtering process. Trials on real laser scans of an EnviSat model demonstrate that the proposed architecture affords at least one order of magnitude better accuracy compared to ICP.
Open Access
Kernel-based fault diagnosis of inertial sensors using analytical redundancy
(2017) Vitanov, Ivan V.; Aouf, Nabil
Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution.