Nabil Aouf

Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.

Robust nonlinear filtering for INS/GPS UAV localization

Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. UAVs rely on accurate location information for a variety of purposes including navigation, motion planning and control, and mission completion. UAV INS/GPS localization is generally used based on navigation filter. Extended Kalman filter is largely used to solve the problem of data fusion and localization, however, EKF suffers from the initialization problem and the linearization errors which severely degrade the performance of the UAV localization estimates. In this paper we propose another innovative alternative, which is based on the Hinfin nonlinear filtering to avoid issues linked with classical filtering techniques and getting a significant robustness. This filtering approach is based on the Hinfin robust control theory, results, comparison with the EKF filter and validation on a simulation of a 3D flight scenario are presented.

Particle Swarm Optimization Inspired Probability Algorithm for Optimal Camera Network Placement

In this paper, a novel method based on binary Particle Swarm Optimization (BPSO) inspired probability is proposed to solve the camera network placement problem. Ensuring accurate visual coverage of the monitoring space with a minimum number of cameras is sought. The visual coverage is defined by realistic and consistent assumptions taking into account camera characteristics. In total, nine evolutionary-like algorithms based on BPSO, Simulated Annealing (SA), Tabu Search (TS) and genetic techniques are adapted to solve this visual coverage based camera network placement problem. All these techniques are introduced in a new and effective framework dealing with constrained optimizations. The performance of BPSO inspired probability technique is compared with the performances of the stochastic variants (e.g., genetic algorithms-based or immune systems-based) of optimization based particle swarm algorithms. Simulation results for 2-D and 3-D scenarios show the efficiency of the proposed technique. Indeed, for a large-scale dimension case, BPSO inspired probability gives better results than the ones obtained by adapting all other variants of BPSO, SA, TS, and genetic techniques.

Robust Airborne 3D Visual Simultaneous Localization and Mapping with Observability and Consistency Analysis

This paper aims to present a robust airborne 3D Visual Simultaneous Localization and Mapping (VSLAM) solution based on a stereovision system. We propose three innovative contributions to the Airborne VSLAM. The first one is the development of an alternative data fusion nonlinear H ∞ filtering scheme. This scheme is based on 3D vision observation model and avoids issues linked with the classical Extended Kalman Filtering (EKF) techniques such as the linearization errors, the initialization problem and noise statistics assumptions. The second contribution consists of a consistency and observability analysis for the Airborne VSLAM. The third contribution is a new approach to map management, based on the k-nearest landmark concept, and allowing efficient loop closure detection and map building. This approach reduces considerably the complexity of our Airborne VSLAM algorithm, which becomes independent of the map landmark number. Simulation results show the efficiency of the proposed Airborne VSLAM solution for which comparisons with other techniques are favourable.

Multispectral Stereo Odometry

In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas-Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss-Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.

Scheduling schemes for an integrated flight and propulsion control system

Abstract We describe two schemes for scheduling an integrated flight and propulsion control system for an experimental vertical/short take-off and landing (V/STOL) aircraft concept in the acceleration from hover (0– 120 kn ) flight phase. Multivariable integrated flight and propulsion controllers are designed at several points over the V/STOL envelope and implemented as exact plant observers with state feedback. In the first scheduling scheme, the values of the state feedback and observer gain matrices are interpolated between the fixed-point designs as a function of aircraft speed. In the second approach, the control signals produced by the different fixed-point controllers are blended, allowing a significant reduction in the order of the scheduled controllers. Both scheduling schemes are shown in non-linear simulation to provide excellent handling qualities as the aircraft accelerates from the hover.

Robust Brightness Description for Computing Optical Flow.

Most optical flow algorithms are based on the assumption of brightness constancy of individual pixels while moving on the image plane. Although being attractive analytically, this assumption is often violated under non-ideal visual conditions resulting in poor flow estimates. This paper presents an approach to support the validity of the assumption under such conditions. The method describes the grey-level of each pixel by the content of its neighbourhood using a geometric moment rather than its individual intensity function value. Then, the description of each pixel is normalised and made insensitive to fluctuations of intensity. As a result, the optical flow algorithm becomes much more reliable and robust against visual phenomena like varying illumination, specular reflections and shadows. The proposed approach is applied to a regression method and comprehensive results on synthetic and real data are reported.

Switched control of a vertical/short take-off land aircraft: An application of linear quadratic bumpless transfer

Abstract This paper describes the design of a switching scheme for integrated flight and propulsion control of a vertical/short take-off land (V/STOL) aircraft throughout the hover and transition regions of its flight envelope. The approach adopted makes use of a recently introduced bumpless transfer technique and presents a methodology for switching between multiple controllers while limiting on-line computational overheads. The success of the switching scheme in maintaining desirable flying qualities as the aircraft accelerates from hover to wing-borne flight is demonstrated in non-linear simulation. Simulation results for the switched system clearly show the effectiveness of the bumpless transfer scheme in preserving closed-loop performance during switching.

Thermal Stereo Odometry for UAVs

In the last decade, visual odometry (VO) has attracted significant research attention within the computer vision community. Most of the works have been carried out using standard visible-band cameras. These sensors offer numerous advantages but also suffer from some drawbacks such as illumination variations and limited operational time (i.e., daytime only). In this paper, we explore techniques that allow us to extend the concepts beyond the visible spectrum. We introduce a localization solution based on a pair of thermal cameras. We focus on VO and demonstrate the accuracy of the proposed solution in daytime as well as night-time. The first challenge with thermal cameras is their geometric calibration. Here, we propose a solution to overcome this issue and enable stereopsis. VO requires a good set of feature correspondences. We use a combination of Fast-Hessian detector with for Fast Retina Keypoint descriptor for that purpose. A range of optimization techniques can be used to compute the incremental motion. Here, we propose the double dogleg algorithm and show that it presents an interesting alternative to the commonly used Levenberg-Marquadt approach. In addition, we explore thermal 3-D reconstruction and show that similar performance to the visible-band can be achieved. In order to validate the proposed solution, we build an innovative experimental setup to capture various data sets, where different weather and time conditions are considered.

Towards efficient distributed service discovery in low-power and lossy networks

Low-power and Lossy Networks (LLNs) have been recognised as a promising technology to achieve ubiquity in the internet of things era. To realise this, service oriented architectures and the emerging IPv6 over low-power wireless personal area network (6LoWPAN) standard are identified as key paradigms. One of the main elements to succeed any service oriented approach is a proficient service discovery protocol. In this paper, we propose EADP: an efficient protocol to announce and discover services in 6LoWPAN networks. EADP adopts a fully distributed approach using an adaptive push–pull model to ensure fast discovery times, low energy consumption and low generated overhead with timely reaction to network dynamics. EADP achieves this by using context-awareness information, delivered by a trickle algorithm. EADP was implemented and evaluated in Contiki using the Cooja simulator. Simulation results show EADP’s capability to realise fast discovery times with low cost in terms of energy and overhead. These achievements make EADP very suitable for pervasive LLN applications.