Abdelkrim Nemra

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 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.

Indoor SLAM for Micro Aerial Vehicles Using Visual and Laser Sensor Fusion

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, such as ruined or semi-demolished buildings. This article aims to investigate a new SLAM technique that fuses laser and visual information, besides measurements from the inertial unit, to robustly obtain the 6DOF pose estimation of a MAV within a local map of the environment. Laser is used to obtain a local 2D map and a footprint estimation of the MAV position, while a monocular visual SLAM algorithm enlarges the pose estimation through an Extended Kalman Filter (EKF). The system consists of a commercial drone and a remote control unit to computationally afford the SLAM algorithms using a distributed node system based on ROS (Robot Operating System). Some experimental results show how sensor fusion improves the position estimation and the obtained map under different test conditions.

Designing embedded systems for fixed-wing UAVs: Dynamic models study for the choice of an emulation vehicle

Fixed-wing Unmanned Aerial Vehicles (UAVs) are a special class of UAVs which present many advantages notably long range of action. Whereas, design of this kind of UAVs requires heavy logistics like outdoor tests, runways, and experimented pilots. These constraints reverberate on the design of embedded systems for fixed-wing UAVs. Because static tests are not representative, this paper proposes a practical approach to evaluate an embedded system on an appropriate vehicle emulating the dynamic model of a fixed-wing aircraft. For that, a comparison between the dynamic model of fixed-wing aircraft, tank-type mobile robot, and a bicycle is achieved. We show that, contrary to trend in literature, a mobile robot is not the optimal choice to emulate a fixed-wing UAV. Indeed, supposing a motion without slip (and a constant altitude for the aircraft), translation models of the three vehicles are under the form of Dubin car model. Whereas, translation and rotation velocities of tank-type mobile robot are coupled (while it is not the case for the aircraft where propulsion and turning are actuated separately). This constraint defines an allowed kinematic zone which limits the emulation of a fixed wing airplane. In the other hand, in bicycle model “bank to turn effect” is similar to the one observed in fixed-wing aircraft model. Furthermore, both models are not defined when the translation velocity tends to zero (stalling effect). As a conclusion, we propose to use mobile robot to test the navigation layer, and the bicycle to evaluate the sensor processing layer of an embedded system based fixed-wing UAVs applications.

3D mapping based VSLAM for UAVs

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 chosen features that are well distributed across the stereo views and refined by RANSAC in order to provide well visualized views for navigation. Experimental results are provided to demonstrate the effectiveness of our 3D mapping strategy.

Robust feature extraction and correspondence for UAV map building

In this paper, a technique to design a robust feature extractor and descriptor for visual map building is proposed. The extracted features are required to be computationally attractive and invariant to image rotation, scale change and illumination. We adapted the Scale Invariant Features Transform (SIFT) algorithm for Map Building applications. Our main contributions are: firstly, we introduce of an adaptive version of the SIFT algorithm suitable for different visual perceptual environments. Secondly, we use of the L-infinity norm as a criterion for feature matching, which ensures more robustness against noises and uncertainties. Finally, we propose a new criterion to select the most stable features in order to improve the visual map building performances. Results based on real images shows the good performance obtained with the proposed approach.

Simultaneous localization and mapping algorithm for unmanned ground vehicle with SVSF filter

Filtering strategies play an important role in estimation theory, and are used to extract knowledge of the true states typically from noisy measurements or observations made of the system. This paper describes a novel approach that combines the information given by an odometer and a laser range finder sensors to efficiently solve the Simultaneous Localization and Mapping (SLAM) problem of the Unmanned Ground Vehicle (UGV) and reconstruct a

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

2D

An adaptive SVSF-SLAM algorithm to improve the success and solving the UGVs cooperation problem

representation of the environment. In recent years, to solve the SLAM problem, many solutions have been proposed. To resolve this problem, the most commonly used approaches are the EKF-SLAM and the FASTSLAM. An accurate process and a model of observation are needed for the first approach, which is suffering the linearization problem. While the second one is not convenient and is not suitable for real time implementation. Therefore, a new state and parameter estimation method is introduced based on the smooth variable structure filter (SVSF) is proposed in this paper to solve the UGV SLAM problem. The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. In this work the SVSF-SLAM algorithm is implemented to construct a map of the environment and localize the UGV within this map. The proposed algorithm is validated and compared to the EKF-SLAM algorithm. Good results are obtained.

Cooperative SLAM for multiple UGVs navigation using SVSF filter

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer P 3 - AT . The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.