Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments

Harmat, Adam; Trentini, Michael; Sharf, Inna

doi:10.1007/s10846-014-0085-y

Cited by 59 publications

(31 citation statements)

References 41 publications

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“…The rest of this section briefly provides an overview of the contributions in this field. Towards this direction in [52], a visual pose estimation system from multiple cameras on-board a UAV, known as Multi-Camera Parallel Tracking and Mapping (PTAM) has been presented. This solution was based on the monocular PTAM and was able to integrate concepts from the field of multi-camera ego-motion estimation.…”

Section: Visual Localization and Mappingmentioning

confidence: 99%

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Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Section: Visual Localization and Mappingmentioning

confidence: 99%

“…A modified SLAM (VSLAM) algorithm is implemented to assist in trajectory tracking for the controller. In [84] a multi camera system for visual odometry is demonstrated. The sensors used are the ultra wide angle fisheye cameras.…”

Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

289

120

View full text Add to dashboard Cite

show abstract

“…Estimation of a small UAV's 6 degree of freedom (6 DOF) pose, relative to its surrounding environment using onboard cameras has also become more important. Results from the field of multi-camera egomotion estimation Schauwecker and Zell (2013) Kim, Hwangbo and Kanade (2008b) Sola, Monin, Devy and VidalCalleja (2008) Kaess and Dellaert (2010) Ragab and Wong (2010) Kim, Hartley, Frahm and Pollefeys (2007) Baker, Fermuller, Aloimonos and Pless (2001) Pless (2003) Harmat, Trentini and Sharf (2015) show that such problem can be better solved by using multiple cameras positioned appropriately. When all cameras have been calibrated with precise positions and attitudes on the robot, it is straightforward to implement algorithms we proposed in Chapter 3 for multi-camera sensor fusion.…”

Section: Extension To Multi-camera Pose Estimationmentioning

confidence: 99%

Analytical SLAM without linearization

Tan

Lohmiller

Slotine

2017

The International Journal of Robotics Research

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We apply a combination of linear time varying (LTV) Kalman filtering and nonlinear contraction tools to the problem of simultaneous mapping and localization (SLAM), in a fashion which avoids linearized approximations altogether. By exploiting virtual synthetic measurements, the LTV Kalman observer avoids errors and approximations brought by the linearization process in the EKF SLAM. Furthermore, conditioned on the robot position, the covariances between landmarks are fully decoupled, making the algorithm easily scalable. Contraction analysis is used to establish stability of the algorithm and quantify its convergence rate. We propose four versions based on different combinations of sensor information, ranging from traditional bearing measurements and radial measurements to optical flows and time-to-contact measurements. As shown in simulations, the proposed algorithm is simple and fast, and it can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.

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“…However, the most common models are the quadcopter and the hexacopter. The structure of this system is generally a fixed frame with an equal distribution of the rotors with respect to the aircraft's centre of mass [5,6,9] . Fixed-wing UAVs system, on the other hand, presents the closest resemblance to the classic avionics models.…”

Section: Introductionmentioning

confidence: 99%

Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review

et al. 2017

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Additive Manufacturing (AM) is a game changing production technology for aerospace applications. Fused deposition modelling is one of the most widely used AM technologies and recently has gained much attention in the advancement of many products. This paper introduces an extensive review of fused deposition modelling and its application in the development of high performance unmanned aerial vehicles. The process methodology, materials, post processing, and properties of its products are discussed in details. Successful examples of using this technology for making functional, lightweight and high endurance unmanned aerial vehicles are also highlighted. In addition, major opportunities, limitations, and outlook of fused deposition modelling are also explored. The paper shows that the emerge of fused deposition modelling as a robust technique for unmanned aerial vehicles represents a good opportunity to produce compact, strong, lightweight structures, and functional parts with embedded electronic.2

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Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments

Cited by 59 publications

References 41 publications

Survey on Computer Vision for UAVs: Current Developments and Trends

Survey on Computer Vision for UAVs: Current Developments and Trends

Analytical SLAM without linearization

Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review

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