Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

Caballero, Fernando; Merino, Luís; Ferruz, J.; Ollero, A.

doi:10.1007/978-1-4020-9137-7_9

Cited by 43 publications

(39 citation statements)

References 35 publications

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“…Another popular technique among researchers currently is vision-based simultaneous localization and mapping (SLAM). [19][20][21] In the study by Robert et al, 22 researchers proposed a new architecture that constructs SLAM maps based on visual data and EKF to navigate in an indoor environment. However, the light detection and ranging (LIDAR) used in the regular SLAM algorithm is costly as well as computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

Ashraf

Aggarwal²,

Damacharla

et al. 2018

International Journal of Distributed Sensor Networks

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The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow-based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system-denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony-based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of 95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.

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Section: Related Workmentioning

confidence: 99%

A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

Ashraf

Aggarwal²,

Damacharla

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Till date, and to the best of the authors' knowledge, airborne 3D VSLAM have rarely been addressed and validated with real data in unknown environments [35][36][37][38]. The solution in this study is validated with real experimental data sets, where only natural landmarks are considered and efficiently detected and matched by a robust adaptive scale-invariant feature transform (SIFT) feature tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

Experimental airborne NH∞ vision-based simultaneous localization and mapping in unknown environments

Nemra

Aouf

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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This article aims to present a robust airborne three-dimensional (3D) visual simultaneous localization and mapping (VSLAM) solution based on a stereovision system embedded in a remotely piloted helicopter. The proposed solution enables an unmanned aerial vehicle to construct a reliable map and localize itself in this map without any user intervention. The main contributions of this article are threefold: first, based on an adaptive scale-invariant feature transform algorithm, a robust approach for feature extraction and matching is proposed. Second, an adaptation of the non-linear H∞ filtering scheme is presented to the VSLAM problem, for which a 3D vision-based observation model is suggested. This filtering scheme avoids issues linked with the classical extended Kalman filtering techniques such as linearization errors, initialization problems, and noise statistics assumptions. Finally, a reliable map management approach, based on the k-nearest landmark concept, is presented to allow efficient loop closing detection and map building. This approach reduces significantly the complexity of the airborne VSLAM algorithm by making it independent of the landmark number. Experimental results using real data sets show the performance and the robustness of the proposed airborne VSLAM solution to build a reliable map and localize the unmanned aerial vehicle within this map.

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“…In this case, features are initialized into the state by using the undelayed inverse depth (UID) method, proposed in [23]. In [24] an homography-based SLAM approach is proposed. In this case homography-based techniques are used to compute the UAV relative translation and rotation by means of the images.…”

Section: Introductionmentioning

confidence: 99%

Delayed Monocular SLAM Approach Applied to Unmanned Aerial Vehicles

2016

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In recent years, many researchers have addressed the issue of making Unmanned Aerial Vehicles (UAVs) more and more autonomous. In this context, the state estimation of the vehicle position is a fundamental necessity for any application involving autonomy. However, the problem of position estimation could not be solved in some scenarios, even when a GPS signal is available, for instance, an application requiring performing precision manoeuvres in a complex environment. Therefore, some additional sensory information should be integrated into the system in order to improve accuracy and robustness. In this work, a novel vision-based simultaneous localization and mapping (SLAM) method with application to unmanned aerial vehicles is proposed. One of the contributions of this work is to design and develop a novel technique for estimating features depth which is based on a stochastic technique of triangulation. In the proposed method the camera is mounted over a servo-controlled gimbal that counteracts the changes in attitude of the quadcopter. Due to the above assumption, the overall problem is simplified and it is focused on the position estimation of the aerial vehicle. Also, the tracking process of visual features is made easier due to the stabilized video. Another contribution of this work is to demonstrate that the integration of very noisy GPS measurements into the system for an initial short period of time is enough to initialize the metric scale. The performance of this proposed method is validated by means of experiments with real data carried out in unstructured outdoor environments. A comparative study shows that, when compared with related methods, the proposed approach performs better in terms of accuracy and computational time.

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Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

Cited by 43 publications

References 35 publications

A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

Experimental airborne NH∞ vision-based simultaneous localization and mapping in unknown environments

Delayed Monocular SLAM Approach Applied to Unmanned Aerial Vehicles

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