Abdelkrim Nemra

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This paper presents an in-depth evaluation of filter algorithms utilized in the estimation of 3D position and attitude for UAV using stereo vision based Visual SLAM integrated with feature detection and matching techniques i.e., SIFT and SURF. The evaluation's aim was to investigate the accuracy and robustness of the filters' estimation for vision based(More)
In the last decade, fuzzy logic has supplanted conventional technologies in some scientific applications and engineering systems especially in control systems, particularly the control of the mobile robots evolving (moving) in completely unknown environments. Fuzzy logic has the ability to express the ambiguity of human thinking and translate expert(More)
This paper represents research in progress in Simultaneous Localization and Mapping (SLAM) for Micro Aerial Vehicles (MAVs) in the context of rescue and/or recognition navigation tasks in indoor environments. In this kind of applications, the MAV must rely on its own onboard sensors to autonomously navigate in unknown, hostile and GPS denied environments,(More)
This paper aims at proposing a framework for Airborne Cooperative Visual Simultaneous Localization and Mapping (C-VSLAM). The use of cooperative vehicles presents many advantages over single-vehicle architecture. We present a nonlinear H<inf>&#x221E;</inf> filtering scheme adapted to multiple Unmanned Aerial Vehicle (UAV) VSLAM based on the extension of a(More)
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion(More)
This paper addresses 3D texture mapping in Visual Simultaneous Localization And Mapping (VSLAM) for Unmanned Aerial Vehicle (UAV) applications. Landmark selection strategy based on feature detection methods such as Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) is adopted. The selected features are combined with additionally(More)