An architecture for robust UAV navigation in GPS‐denied areas

Pérez-Grau, Francisco J.; Ragel, Ricardo; Caballero, Fernando; Viguria, Antidio; Ollero, A.

doi:10.1002/rob.21757

Cited by 67 publications

(37 citation statements)

References 49 publications

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“…In other methods the optimization concentrates only on robot pose assuming that the scenario map is available. Work [16] uses an AMCL (Adaptive Monte Carlo Localization) for pose estimation analyzing the consistency of RGB-D camera measurements with a precomputed map. AMCL filters have high computational burden that is often addressed by using simple likelihood functions that do not always fully exploit the information contained in the features.…”

Section: Related Workmentioning

confidence: 99%

Multi-Sensor 6-DoF Localization For Aerial Robots In Complex GNSS-Denied Environments

Paneque

Dios

Ollero

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The need for robots autonomously navigating in more and more complex environments has motivated intense R&D efforts in making robot pose estimation more accurate and reliable. This paper presents a multi-sensor multi-hypothesis method for robust 6-DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multi-hypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multi-core implementation. The proposed method has been extensively validated for closedloop aerial robot navigation in different urban and industrial scenarios and has shown advantages over well-known singlesensor techniques.

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Section: Related Workmentioning

confidence: 99%

Multi-Sensor 6-DoF Localization For Aerial Robots In Complex GNSS-Denied Environments

Paneque

Dios

Ollero

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In addition, ego-motion estimation in vehicles and robots is fundamental as it is usually the pre-requisite for higher-layer tasks, such as robot-based surveillance, autonomous navigation, path planning, for example, References [6,7]. A vision-based odometry system, compared to a traditional wheel-based or satellites-based localization system, has the advantages of an impervious character to inherent sensor inefficacies [8,9] (e.g., wheel encoder error because of uneven, slippery terrain or other adverse conditions) and can be used in a GPS-denied area [10,11] (e.g., underwater and tunnels in urban environments.) The proposed approach utilizes only visual perception cameras with lightweight, high robustness and low-cost characters.…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

et al. 2019

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We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.

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“…As such, our approach uses the Software In The Loop (SITL) [8] stack, commonly used to simulate the flight control of a single or multiagent UAV [8], [11]. The navigational approach presented in this paper is also independent of the Global Positioning System (GPS), mainly due to the low reliability of GPS signals under dense forest canopy [7]. The proposed learningbased approach utilises a very simple and light-weight network architecture 1 and robustly generalises to new unstructured and unseen environments.…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

Maciel-Pearson

Akçay

Atapour-Abarghouei

et al. 2019

IEEE Robot. Autom. Lett.

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Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques.Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

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An architecture for robust UAV navigation in GPS‐denied areas

Cited by 67 publications

References 49 publications

Multi-Sensor 6-DoF Localization For Aerial Robots In Complex GNSS-Denied Environments

Multi-Sensor 6-DoF Localization For Aerial Robots In Complex GNSS-Denied Environments

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments

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